Suchergebnis: Katalogdaten im Frühjahrssemester 2018

Rechnergestützte Wissenschaften Master Information
Kernfächer
Von den im HS und FS angebotenen Kernfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden.
NummerTitelTypECTSUmfangDozierende
401-3632-00LComputational StatisticsW10 KP3V + 2UM. H. Maathuis
KurzbeschreibungComputational Statistics deals with modern statistical methods of data analysis (aka "data science") for prediction and inference. The course provides an overview of existing methods. The course is hands-on, and methods are applied using the statistical programming language R.
LernzielIn this class, the student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R.
Voraussetzungen / BesonderesAt least one semester of (basic) probability and statistics.

Programming experience is helpful but not required.
263-2300-00LHow To Write Fast Numerical Code Information Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
Number of participants limited to 84.

Prerequisite: Master student, solid C programming skills.
W6 KP3V + 2UM. Püschel
KurzbeschreibungThis course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for numerical functionality such as linear algebra and others. The focus is on optimizing for the memory hierarchy and for special instruction sets. Finally, the course will introduce the recent field of automatic performance tuning.
LernzielSoftware performance (i.e., runtime) arises through the interaction of algorithm, its implementation, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this interaction, and hence software performance, focusing on numerical or mathematical functionality. The second goal is to teach a general systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in a few homeworks and semester-long group projects.
InhaltThe fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture.

This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance software development using important functionality such as linear algebra functionality, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and, if time permits, how to write multithreaded code for multicore platforms. Much of the material is based on state-of-the-art research.

Further, a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. Finally, the course will introduce the students to the recent field of automatic performance tuning.
Vertiefungsgebiete
Astrophysik
NummerTitelTypECTSUmfangDozierende
402-0394-00LTheoretical Astrophysics and Cosmology
Studierende der UZH dürfen diese Lerneinheit nicht an der ETH belegen, sondern müssen das entsprechende Modul direkt an der UZH buchen.
W10 KP4V + 2UL. M. Mayer, J. Yoo
KurzbeschreibungThis is the second of a two course series which starts with "General Relativity" and continues in the spring with "Theoretical Astrophysics and Cosmology", where the focus will be on applying general relativity to cosmology as well as developing the modern theory of structure formation in a cold dark matter Universe.
Lernziel
InhaltThe course will cover the following topics:
- Homogeneous cosmology
- Thermal history of the universe, recombination, baryogenesis and nucleosynthesis
- Dark matter and Dark Energy
- Inflation
- Perturbation theory: Relativistic and Newtonian
- Model of structure formation and initial conditions from Inflation
- Cosmic microwave background anisotropies
- Spherical collapse and galaxy formation
- Large scale structure and cosmological probes
LiteraturSuggested textbooks:
H.Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution
S. Carroll: Space-Time and Geometry: An Introduction to General Relativity
S. Dodelson: Modern Cosmology
Secondary textbooks:
S. Weinberg: Gravitation and Cosmology
V. Mukhanov: Physical Foundations of Cosmology
E. W. Kolb and M. S. Turner: The Early Universe
N. Straumann: General relativity with applications to astrophysics
A. Liddle and D. Lyth: Cosmological Inflation and Large Scale Structure
Voraussetzungen / BesonderesKnowledge of General Relativity is recommended.
Atmosphärenphysik
NummerTitelTypECTSUmfangDozierende
701-1216-00LNumerical Modelling of Weather and Climate Information W4 KP3GC. Schär, U. Lohmann
KurzbeschreibungThe guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes.
LernzielThe guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes.
InhaltThe course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction.

Hands-on experience with simple models will be acquired in the tutorials.
SkriptSlides and lecture notes will be made available at
Link
LiteraturList of literature will be provided.
Voraussetzungen / BesonderesPrerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming
701-1232-00LRadiation and Climate ChangeW3 KP2GM. Wild, W. Ball
KurzbeschreibungThis lecture focuses on the prominent role of radiation in the energy balance of the Earth and in the context of past and future climate change.
LernzielThe aim of this course is to develop a thorough understanding of the fundamental role of radiation in the context of climate change.
InhaltThe course will cover the following topics:
Basic radiation laws; sun-earth relations; the sun as driver of climate change (faint sun paradox, Milankovic ice age theory, solar cycles); radiative forcings in the atmosphere: aerosol, water vapour, clouds; radiation balance of the Earth (satellite and surface observations, modeling approaches); anthropogenic perturbation of the Earth radiation balance: greenhouse gases and enhanced greenhouse effect, air pollution and global dimming; radiation-induced feedbacks in the climate system (water vapour feedback, snow albedo feedback); climate model scenarios under various radiative forcings.
SkriptSlides will be made available, lecture notes for part of the course
LiteraturAs announced in the course
701-1228-00LCloud Dynamics: Hurricanes Information W4 KP3GU. Lohmann
KurzbeschreibungHurricanes are among the most destructive elements in the atmosphere. This lecture will discuss the physical requirements for their formation, life cycle, damage potential and their relationship to global warming. It also distinguishes hurricanes from thunderstorms and tornadoes.
LernzielAt the end of this course students will be able to distinguish the formation and life cycle mechanisms of tropical cyclones from those of extratropical thunderstorms/cyclones, project how tropical cyclones change in a warmer climate based on their physics and evaluate different tropical cyclone modification ideas.
SkriptSlides will be made available
LiteraturA literature list can be found here: Link
Voraussetzungen / BesonderesAt least one introductory lecture in Atmospheric Science or Instructor's consent.
401-5930-00LSeminar in Physics of the Atmosphere for CSEW4 KP2SH. Joos, C. Schär
KurzbeschreibungIn this seminar the knowledge exchange between you and the other students is promoted. You attend lectures on scientific writing and you train your scientific writing skills by writing a proposal for your MSc thesis. You receive critical and constructive feedback through an in-depth review process by scientific writing experts and your future supervisors.
LernzielIn this seminar the knowledge exchange between you and the other students is promoted. You attend lectures on scientific writing and you train your scientific writing skills by writing a proposal for your MSc thesis. You receive critical and constructive feedback through an in-depth review process by scientific writing experts and your future supervisors.
Chemie
NummerTitelTypECTSUmfangDozierende
529-0474-00LQuantenchemieW6 KP3GM. Reiher, T. Weymuth
KurzbeschreibungEinführung in Konzepte der Elektronenstruktur-Theorie und in die Methoden der numerischen Quantenchemie; begleitende Übungen mit Papier und Bleistift, sowie Anleitungen zu praktischen Berechnungen mit Quantenchemie-Programmen am Computer.
LernzielChemie kann inzwischen vollständig am Computer betrieben werden, eine intellektuelle Leistung, für die 1998 der Nobelpreis an Pople und Kohn verliehen wurde. Diese Vorlesung zeigt, wie das geht. Erarbeitet wird dabei die Vielteilchen-Quantentheorie von Mehrelektronensystemen (Atome und Molekuele) und ihre Implementierung in Computerprogramme. Es soll ein vollständiges Bild der Quantenchemie vermittelt werden, das alles Rüstzeug zur Verfügung stellt, um selbst solche Berechnungen durchführen zu können (sei es begleitend zum Experiment oder als Start in eine Vertiefung dieser Theorie).
InhaltGrundlegende Konzepte der Vielteilchen-Quantenmechanik. Entwicklung der Mehrelektronentheorie für Atome und Molekuele; beginnend bei der harmonischen Naeherung für das Kern-Problem und bei der Hartree-Fock-Theorie für das elektronische Problem über Moeller-Plesset-Stroerungstheorie und Konfigurationswechselwirkung zu Coupled-Cluster und Multikonfigurationsverfahren. Dichtefunktionaltheorie. Verwendung quantenchemischer Software und Problemlösungen mit dem Computer.
SkriptEin Skript zu allen Vorlesungsstunden wird zur Verfügung gestellt (die aufgearbeitete Theorie wird durch praktische Beispiele kontinuierlich begleitet).
LiteraturLehrbücher:
F.L. Pilar, Elementary Quantum Chemistry, Dover Publications
I.N. Levine, Quantum Chemistry, Prentice Hall

Hartree-Fock in Basisdarstellung:
A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill

Bücher zur Computerchemie:
F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons
C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons
Voraussetzungen / BesonderesVoraussetzungen:einführende Vorlesung in Quantenmechanik (z.B. Physikalische Chemie III: Quantenmechanik)
327-0613-00LComputer Applications: Finite Elements in Solids and Structures Information
The course will only take place if at least 7 students are enrolled.
W4 KP2V + 2UA. Gusev
KurzbeschreibungEinführung in die Finite-Elemente-Methode für Studenten mit einem allgemeinen Interesse an diesem Gebiet
LernzielEinführung in die Finite-Elemente-Methode für Studenten mit einem allgemeinen Interesse in diesem Gebiet
InhaltEinführung, Energieformulierungen, die Rayleigh-Ritz-Methode, Finite-Elemente der Verschiebungen, Lösungen zu den Finite-Elemente Gleichungen, Lineare Elemente, Konvergenz, Kompatibilität und Vollständigkeit, Finite Elemente höherer Ordnung, Beam- und Frame-Elemente, Plate- und Shell-Elemente, Dynamik und Vibrationen, Verallgemeinerung des Finite-Elemente-Konzeptes (Galerkin-weighted residual and variational approaches)
SkriptAutographie
Literatur- Astley R.J. Finite Elements in Solids and Structures, Chapman & Hill, 1992
- Zienkiewicz O.C., Taylor R.L. The Finite Element Method, 5th ed., vol. 1, Butterworth-Heinemann, 2000
401-5940-00LSeminar in Chemistry for CSEW4 KP2SP. H. Hünenberger, M. Reiher
KurzbeschreibungThe student will carry out a literature study on a topic of his or her liking or suggested by the supervisor in the area of computer simulation in chemistry, the results of which are to be presented both orally and in written form.

For more information: Link
Lernziel
Fluiddynamik
Eine der beiden Lerneinheiten
151-0208-00L Berechnungsmethoden der Energie- und Verfahrenstechnik
151-0212-00L Advanced CFD Methods
ist obligatorisch.
NummerTitelTypECTSUmfangDozierende
151-0208-00LBerechnungsmethoden der Energie- und VerfahrenstechnikO4 KP2V + 2UD. W. Meyer-Massetti
KurzbeschreibungEs werden numerische Methoden für Berechnungsaufgaben der Fluiddynamik, Energie- und Verfahrenstechnik dargestellt und an einfachen Beispielen auf dem Rechner geübt.
Inhalt: Problemlösungsprozess, physikalische und mathematische Modelle, Grundgleichungen, Diskretisierungsverfahren, numerische Lösung der Advektionsgleichung, Diffusionsgleichung und Poisson-Gleichung, turbulente Strömungen.
LernzielKenntnisse und praktische Erfahrungen mit der Anwendung der wichtigsten Diskretisierungs- und Lösungsverfahren für Berechnungsaufgaben der Fluiddynamik und der Energie- und Verfahrenstechnik
InhaltAufbauend auf den Lehrveranstaltungen über Fluiddynamik, Thermodynamik, Computational Methods for Engineering Application I (empfehlenswertes Wahlfach, 4. Semester) und Informatik (Programmieren) werden numerische Methoden für Berechnungsaufgaben der Fluiddynamik, Energie- und Verfahrenstechnik dargestellt und an einfachen Beispielen geübt.

1. Einleitung
Übersicht, Anwendungen
Problemlösungsprozess, Fehler
2. Rekapitulation der Grundgleichungen
Formulierung, Anfangs- und Randbedingungen
3. Numerische Diskretisierungsverfahren
Finite-Differenzen- und Finite-Volumen-Verfahren
Grundbegriffe: Konsistenz, Stabilität, Konvergenz
4. Lösung der grundlegenden Gleichungstypen
Wärmeleitungs/Diffusionsgleichung (parabolisch)
Poisson-Gleichung (elliptisch)
Advektionsgleichung/Wellengleichung (hyperbolisch)
und Advektions-Diffusions-Gleichung
5. Berechnung inkompressibler Strömungen
6. Berechnung turbulenter Strömungen
SkriptEin Skript steht zur Verfügung
Literaturwird zu Beginn der Vorlesung mitgeteilt
Voraussetzungen / BesonderesÜbungen:
Es werden theoretische und praktische (Programmier-)Aufgaben mit Anwendungen aus Fluiddynamik, Energie- und Verfahrenstechnik gestellt. Eine aktive Teilnahme ist unerlässlich.
Grundkenntnisse in Matlab sind von Vorteil.
151-0212-00LAdvanced CFD MethodsW4 KP2V + 1UP. Jenny
KurzbeschreibungFundamental and advanced numerical methods used in commercial and open-source CFD codes will be explained. The main focus is on numerical methods for conservation laws with discontinuities, which is relevant for trans- and hypersonic gas dynamics problems, but also CFD of incompressible flows, Direct Simulation Monte Carlo and the Lattice Boltzmann method are explained.
LernzielKnowing what's behind a state-of-the-art CFD code is not only important for developers, but also for users in order to choose the right methods and to achieve meaningful and accurate numerical results. Acquiring this knowledge is the main goal of this course.

Established numerical methods to solve the incompressible and compressible Navier-Stokes equations are explained, whereas the focus lies on finite volume methods for compressible flow simulations. In that context, first the main theory and then numerical schemes related to hyperbolic conservation laws are explained, whereas not only examples from fluid mechanics, but also simpler, yet illustrative ones are considered (e.g. Burgers and traffic flow equations). In addition, two less commonly used yet powerful approaches, i.e., the Direct Simulation Monte Carlo (DSMC) and Lattice Boltzmann methods, are introduced.

For most exercises a C++ code will have to be modified and applied.
Inhalt- Finite-difference vs. finite-element vs. finite-volume methods
- Basic approach to simulate incompressible flows
- Brief introduction to turbulence modeling
- Theory and numerical methods for compressible flow simulations
- Direct Simulation Monte Carlo (DSMC)
- Lattice Boltzmann method
SkriptPart of the course is based on the referenced books. In addition, the participants receive a manuscript and the slides.
Literatur"Computational Fluid Dynamics" by H. K. Versteeg and W. Malalasekera.
"Finite Volume Methods for Hyperbolic Problems" by R. J. Leveque.
Voraussetzungen / BesonderesBasic knowledge in
- fluid dynamics
- numerical mathematics
- programming (programming language is not important, but C++ is of advantage)
151-0110-00LCompressible FlowsW4 KP2V + 1UJ.‑P. Kunsch
KurzbeschreibungThemen: Instationäre eindimensionale Unterschall- und Überschallströmungen, Akustik, Schallausbreitung, Überschallströmung mit Stössen und Prandtl-Meyer Expansionen, Umströmung von schlanken Körpern, Stossrohre, Reaktionsfronten (Deflagration und Detonation).
Mathematische Werkzeuge: Charakteristikenverfahren, ausgewählte numerische Methoden.
LernzielIllustration der Physik der kompressiblen Strömungen und Üben der mathematischen Methoden anhand einfacher Beispiele.
InhaltDie Kompressibilität im Zusammenspiel mit der Trägheit führen zu Wellen in einem Fluid. So spielt die Kompressibilität bei instationären Vorgängen (Schwingungen in Gasleitungen, Auspuffrohren usw.) eine wichtige Rolle. Auch bei stationären Unterschallströmungen mit hoher Machzahl oder bei Überschallströmungen muss die Kompressibilität berücksichtigt werden (Flugtechnik, Turbomaschinen usw.).
In dem ersten Teil der Vorlesung wird die Wellenausbreitung bei eindimensionalen Unterschall- und Überschallströmungen behandelt. Es werden sowohl Wellen kleiner Amplitude in akustischer Näherung, als auch Wellen grosser Amplitude mit Stossbildung behandelt.

Der zweite Teil befasst sich mit ebenen stationären Überschallströmungen. Schlanke Körper in einer Parallelströmung werden als schwache Störungen der Strömung angesehen und können mit den Methoden der Akustik behandelt werden. Zu der Beschreibung der zweidimensionalen Überschallumströmung beliebiger Körper gehören schräge Verdichtungsstösse, Prandtl -Meyer Expansionen usw.. Unterschiedliche Randbedingungen (Wände usw.) und Wechselwirkungen, Reflexionen werden berücksichtigt.
Skriptnicht verfügbar
LiteraturEine Literaturliste mit Buchempfehlungen wird am Anfang der Vorlesung ausgegeben.
Voraussetzungen / BesonderesVoraussetzungen: Fluiddynamik I und II
401-5950-00LSeminar in Fluid Dynamics for CSE Belegung eingeschränkt - Details anzeigen W4 KP2SP. Jenny, T. Rösgen
KurzbeschreibungEnlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics
LernzielEnlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics
Voraussetzungen / BesonderesContact Prof. P. Jenny or Prof. T. Rösgen before the beginning of the semester
Systems and Control
NummerTitelTypECTSUmfangDozierende
227-0216-00LControl Systems II Information W6 KP4GR. Smith
KurzbeschreibungIntroduction to basic and advanced concepts of modern feedback control.
LernzielIntroduction to basic and advanced concepts of modern feedback control.
InhaltThis course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.
SkriptThe slides of the lecture are available to download.
LiteraturSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Voraussetzungen / BesonderesPrerequisites:
Control Systems or equivalent
227-0224-00LStochastic Systems Information W4 KP2V + 1UF. Herzog
KurzbeschreibungProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
LernzielStochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance.
Inhalt- Stochastic processes
- Stochastic calculus (Ito)
- Stochastic differential equations
- Discrete time stochastic difference equations
- Stochastic processes AR, MA, ARMA, ARMAX, GARCH
- Kalman filter
- Stochastic optimal control
- Applications in finance and engineering
SkriptH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
227-0207-00LNonlinear Systems and Control Information
Voraussetzung: Control Systems (227-0103-00L)
W6 KP4GE. Gallestey Alvarez, P. F. Al Hokayem
KurzbeschreibungIntroduce students to the area of nonlinear systems and their control. Familiarize them with tools for modelling and analysis of nonlinear systems. Provide an overview of the various nonlinear controller design methods.
LernzielOn completion of the course, students understand the difference between linear and nonlinear systems, know the the mathematical techniques for modeling and analysing these systems, and have learnt various methods for designing controllers for these systems.
Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of virtues and drawbacks in the different methods.
InhaltVirtually all practical control problems are of nonlinear nature. In some cases the application of linear control methods will lead to satisfying controller performance. In many other cases however, only application of nonlinear analysis and synthesis methods will guarantee achievement of the desired objectives. During the past decades a number of mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for modelling and analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons, and the students will be familiarized with the basic concepts of nonlinear control theory.

This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control.
SkriptAn english manuscript will be made available on the course homepage during the course.
LiteraturH.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Voraussetzungen / BesonderesPrerequisites: Linear Control Systems, or equivalent.
401-4938-14LStochastic Optimal Control Information
Findet dieses Semester nicht statt.
W4 KP2VM. Soner
KurzbeschreibungDynamic programming approach to stochastic optimal control problems will be developed. In addition to the general theory, detailed analysis of several important control problems will be given.
LernzielGoals are to achieve a deep understanding of

1. Dynamic programming approach to optimal control;
2. Several classes of important optimal control problems and their solutions.
3. To be able to use this models in engineering and economic modeling.
InhaltIn this course, we develop the dynamic programming approach for the stochastic optimal control problems. The general approach will be described and several subclasses of problems will also be discussed in including:
1. Standard exit time problems;
2. Finite and infinite horizon problems;
3. Optimal stoping problems;
4. Singular problems;
5. Impulse control problems.

After the general theory is developed, it will be applied to several classical problems including:
1. Linear quadratic regulator;
2. Merton problem for optimal investment and consumption;
3. Optimal dividend problem of (Jeanblanc and Shiryayev);
4. Finite fuel problem;
5. Utility maximization with transaction costs;
6. A deterministic differential game related to geometric flows.

Textbook will be

Controlled Markov Processes and Viscosity Solutions, 2nd edition, (W.H. Fleming and H.M. Soner) Springer-Verlag, (2005).

And lecture notes will be provided.
LiteraturControlled Markov Processes and Viscosity Solutions, 2nd edition, (W.H. Fleming and H.M. Soner) Springer-Verlag, (2005).

And lecture notes will be provided.
Voraussetzungen / BesonderesBasic knowledge of Brownian motion, stochastic differential equations and probability theory is needed.
401-5850-00LSeminar in Systems and Control for CSEW4 KP2SJ. Lygeros
KurzbeschreibungCourse based on individual study. Short projects involving literature review, possibly simple research tasks.
LernzielIntroduce students to state of the art research in systems and control.
Robotik
NummerTitelTypECTSUmfangDozierende
151-0854-00LAutonomous Mobile Robots Information W5 KP4GR. Siegwart, M. Chli, J. Nieto
KurzbeschreibungThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
LernzielThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, envionmen perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
SkriptThis lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
LiteraturThis lecture is based on the Textbook:
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
151-0566-00LRecursive Estimation Information W4 KP2V + 1UR. D'Andrea
KurzbeschreibungEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
LernzielLearn the basic recursive estimation methods and their underlying principles.
InhaltIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
SkriptLecture notes available on course website: Link
Voraussetzungen / BesonderesRequirements: Introductory probability theory and matrix-vector algebra.
252-0220-00LIntroduction to Machine Learning Information
Previously called Learning and Intelligent Systems

Prof. Krause approves that students take distance exams, also if the exam will take place at a later time due to a different time zone of the alternative exam place.
To get Prof. Krause's signature on the distance exam form please send it to Rita Klute, Link.
W8 KP4V + 2U + 1AA. Krause
KurzbeschreibungThe course introduces the foundations of learning and making predictions based on data.
LernzielThe course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Inhalt- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent)
- Linear classification: Logistic regression (feature selection, sparsity, multi-class)
- Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression; k-NN
- The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)
- Statistical decision theory (decision making based on statistical models and utility functions)
- Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)
- Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)
- Bayesian networks and exact inference (conditional independence; variable elimination; TANs)
- Approximate inference (sum/max product; Gibbs sampling)
- Latent variable models (Gaussian Misture Models, EM Algorithm)
- Temporal models (Bayesian filtering, Hidden Markov Models)
- Sequential decision making (MDPs, value and policy iteration)
- Reinforcement learning (model-based RL, Q-learning)
LiteraturTextbook: Kevin Murphy: A Probabilistic Perspective, MIT Press
Voraussetzungen / BesonderesDesigned to provide basis for following courses:
- Advanced Machine Learning
- Data Mining: Learning from Large Data Sets
- Probabilistic Artificial Intelligence
- Probabilistic Graphical Models
- Seminar "Advanced Topics in Machine Learning"
252-0579-00L3D Vision Information W4 KP3GT. Sattler, M. R. Oswald
KurzbeschreibungThe course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.
LernzielAfter attending this course, students will:
1. understand the core concepts for recovering 3D shape of objects and scenes from images and video.
2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications.
3. have a good overview over the current state-of-the art in 3D vision.
4. be able to critically analyze and asses current research in this area.
InhaltThe goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.
401-5860-00LSeminar in Robotics for CSEW4 KP2SR. Siegwart
KurzbeschreibungThis course provides an opportunity to familiarize yourself with the advanced topics of robotics and mechatronics research. The seminar consists of a literature study, including a report and a presentation.
LernzielThe students are familiar with the challenges of the fascinating and interdisciplinary field of Robotics and Mechatronics. They are introduced in the basics of independent non-experimental scientific research and are able to summarize and to present the results efficiently.
InhaltThis 4 ECTS course requires each student to discuss a study plan with the lecturer and select minimum 10 relevant scientific publications to read through. At the end of semester, the results should be presented in an oral presentation and summarized in a report.
Physik
Für das Vertiefungsgebiet "Physik" sind Grundkenntnisse in Quantenmechnik erforderlich.
NummerTitelTypECTSUmfangDozierende
402-0812-00LComputational Statistical Physics Information W8 KP2V + 2UH. J. Herrmann
KurzbeschreibungSimulationsmethoden in der statistischen Physik. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung.
LernzielDie Vorlesung ist eine Vertiefung von Simulationsmethoden in der statistischen Physik, und daher ideal als Fortführung der Veranstaltung "Introduction to Computational Physics" des Herbstsemesters mit folgenden Schwerpunkten. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung.
InhaltSimulationsmethoden in der statistischen Physik.
Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung.
402-0810-00LComputational Quantum PhysicsW8 KP2V + 2UA. Soluyanov
KurzbeschreibungThis course provides an introduction to simulation methods for quantum systems, starting with the one-body problem and finishing with quantum field theory, with special emphasis on quantum many-body systems. Both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, quantum Monte Carlo) are covered.
LernzielThe goal is to become familiar with computer simulation techniques for quantum physics, through lectures and practical programming exercises.
402-0448-01LQuantum Information Processing I: Concepts
Dieser theoretisch ausgerichtete Teil QIP I bildet zusammen mit dem experimentell ausgerichteten Teil 402-0448-02L QIP II, die beide im Frühjahrssemester angeboten werden, das experimentelle Kernfach "Quantum Information Processing" mit total 10 ECTS-Kreditpunkten.
W5 KP2V + 1UL. Pacheco Cañamero B. del Rio
KurzbeschreibungThe course will cover the key concepts and ideas of quantum information processing, including descriptions of quantum algorithms which give the quantum computer the power to compute problems outside the reach of any classical supercomputer. Key concepts such as quantum error correction will be described. These ideas provide fundamental insights into the nature of quantum states and measurement.
LernzielWe aim to provide an overview of the central concepts in Quantum Information Processing, including insights into the advantages to be gained from using quantum mechanics and the range of techniques based on quantum error correction which enable the elimination of noise.
InhaltThe topics covered in the course will include
1. Entanglement
2. Circuits, circuit elements, universality
3. Efficiency ideas, Gottesmann Knill
4. Teleportation + dense coding
5. Swapping/Gate Teleportation
6. Algorithms: Shor, Grover,
7. Deutsch-Josza, simulations of local systems
8. Cryptography
9. Error correction, basic circuit,
10. ideas of construction, Fault-tolerant design,
SkriptWill be made available on the Moodle for the course. More details to follow.
LiteraturQuantum Computation and Quantum Information
Michael Nielsen and Isaac Chuang
Cambridge University Press
402-0448-02LQuantum Information Processing II: Implementations
Dieser experimentell ausgerichtete Teil QIP II bildet zusammen mit dem theoretisch ausgerichteten Teil 402-0448-01L QIP I, die beide im Frühjahrssemester angeboten werden, das experimentelle Kernfach "Quantum Information Processing" mit total 10 ECTS-Kreditpunkten.
W5 KP2V + 1UA. Wallraff
KurzbeschreibungIntroduction to experimental systems for quantum information processing (QIP). Quantum bits. Coherent Control. Measurement. Decoherence. Microscopic and macroscopic quantum systems. Nuclear magnetic resonance (NMR). Photons. Ions and neutral atoms in electromagnetic traps. Charges and spins in quantum dots and NV centers. Charges and flux quanta in superconducting circuits. Novel hybrid systems.
LernzielThroughout the past 20 years the realm of quantum physics has entered the domain of information technology in more and more prominent ways. Enormous progress in the physical sciences and in engineering and technology has allowed us to build novel types of information processors based on the concepts of quantum physics. In these processors information is stored in the quantum state of physical systems forming quantum bits (qubits). The interaction between qubits is controlled and the resulting states are read out on the level of single quanta in order to process information. Realizing such challenging tasks is believed to allow constructing an information processor much more powerful than a classical computer. This task is taken on by academic labs, startups and major industry. The aim of this class is to give a thorough introduction to physical implementations pursued in current research for realizing quantum information processors. The field of quantum information science is one of the fastest growing and most active domains of research in modern physics.
InhaltIntroduction to experimental systems for quantum information processing (QIP).
- Quantum bits
- Coherent Control
- Measurement
- Decoherence
QIP with
- Ions
- Superconducting Circuits
- Photons
- NMR
- Rydberg atoms
- NV-centers
- Quantum dots
SkriptCourse material be made available at Link and on the Moodle platform for the course. More details to follow.
LiteraturQuantum Computation and Quantum Information
Michael Nielsen and Isaac Chuang
Cambridge University Press
Voraussetzungen / BesonderesThe class will be taught in English language.

Basic knowledge of concepts of quantum physics and quantum systems, e.g from courses such as Phyiscs III, Quantum Mechanics I and II or courses on topics such as atomic physics, solid state physics, quantum electronics are considered helpful.

More information on this class can be found on the web site Link
327-5102-00LMolecular and Materials Modelling Information W4 KP2V + 2UD. Passerone, C. Pignedoli
Kurzbeschreibung"Molecular and Materials Modelling" introduces the basic techniques to interpret experiments with contemporary atomistic simulation. These techniques include force fields or density functional theory (DFT) based molecular dynamics and Monte Carlo. Structural and electronic properties, thermodynamic and kinetic quantities, and various spectroscopies will be simulated for nanoscale systems.
LernzielThe ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, STM, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment.
SkriptA script will be made available.
LiteraturD. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002.

M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990.

Andrew R. Leach, Molecular Modelling, principles and applications, Pearson, 2001
529-0474-00LQuantenchemieW6 KP3GM. Reiher, T. Weymuth
KurzbeschreibungEinführung in Konzepte der Elektronenstruktur-Theorie und in die Methoden der numerischen Quantenchemie; begleitende Übungen mit Papier und Bleistift, sowie Anleitungen zu praktischen Berechnungen mit Quantenchemie-Programmen am Computer.
LernzielChemie kann inzwischen vollständig am Computer betrieben werden, eine intellektuelle Leistung, für die 1998 der Nobelpreis an Pople und Kohn verliehen wurde. Diese Vorlesung zeigt, wie das geht. Erarbeitet wird dabei die Vielteilchen-Quantentheorie von Mehrelektronensystemen (Atome und Molekuele) und ihre Implementierung in Computerprogramme. Es soll ein vollständiges Bild der Quantenchemie vermittelt werden, das alles Rüstzeug zur Verfügung stellt, um selbst solche Berechnungen durchführen zu können (sei es begleitend zum Experiment oder als Start in eine Vertiefung dieser Theorie).
InhaltGrundlegende Konzepte der Vielteilchen-Quantenmechanik. Entwicklung der Mehrelektronentheorie für Atome und Molekuele; beginnend bei der harmonischen Naeherung für das Kern-Problem und bei der Hartree-Fock-Theorie für das elektronische Problem über Moeller-Plesset-Stroerungstheorie und Konfigurationswechselwirkung zu Coupled-Cluster und Multikonfigurationsverfahren. Dichtefunktionaltheorie. Verwendung quantenchemischer Software und Problemlösungen mit dem Computer.
SkriptEin Skript zu allen Vorlesungsstunden wird zur Verfügung gestellt (die aufgearbeitete Theorie wird durch praktische Beispiele kontinuierlich begleitet).
LiteraturLehrbücher:
F.L. Pilar, Elementary Quantum Chemistry, Dover Publications
I.N. Levine, Quantum Chemistry, Prentice Hall

Hartree-Fock in Basisdarstellung:
A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill

Bücher zur Computerchemie:
F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons
C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons
Voraussetzungen / BesonderesVoraussetzungen:einführende Vorlesung in Quantenmechanik (z.B. Physikalische Chemie III: Quantenmechanik)
401-5810-00LSeminar in Physics for CSEW4 KP2SA. Soluyanov
KurzbeschreibungIn this seminar the students present a talk on an advanced topic in modern theoretical or computational physics.
Lernziel
Computational Finance
NummerTitelTypECTSUmfangDozierende
401-4658-00LComputational Methods for Quantitative Finance: PDE Methods Information Belegung eingeschränkt - Details anzeigen W6 KP3V + 1UC. Schwab
KurzbeschreibungIntroduction to principal methods of option pricing. Emphasis on PDE-based methods. Prerequisite MATLAB programming
and knowledge of numerical mathematics at ETH BSc level.
LernzielIntroduce the main methods for efficient numerical valuation of derivative contracts in a
Black Scholes as well as in incomplete markets due Levy processes or due to stochastic volatility
models. Develop implementation of pricing methods in MATLAB.
Finite-Difference/ Finite Element based methods for the solution of the pricing integrodifferential equation.
Inhalt1. Review of option pricing. Wiener and Levy price process models. Deterministic, local and stochastic
volatility models.
2. Finite Difference Methods for option pricing. Relation to bi- and multinomial trees.
European contracts.
3. Finite Difference methods for Asian, American and Barrier type contracts.
4. Finite element methods for European and American style contracts.
5. Pricing under local and stochastic volatility in Black-Scholes Markets.
6. Finite Element Methods for option pricing under Levy processes. Treatment of
integrodifferential operators.
7. Stochastic volatility models for Levy processes.
8. Techniques for multidimensional problems. Baskets in a Black-Scholes setting and
stochastic volatility models in Black Scholes and Levy markets.
9. Introduction to sparse grid option pricing techniques.
SkriptThere will be english, typed lecture notes as well as MATLAB software for registered participants in the course.
LiteraturR. Cont and P. Tankov : Financial Modelling with Jump Processes, Chapman and Hall Publ. 2004.

Y. Achdou and O. Pironneau : Computational Methods for Option Pricing, SIAM Frontiers in Applied Mathematics, SIAM Publishers, Philadelphia 2005.

D. Lamberton and B. Lapeyre : Introduction to stochastic calculus Applied to Finance (second edition), Chapman & Hall/CRC Financial Mathematics Series, Taylor & Francis Publ. Boca Raton, London, New York 2008.

J.-P. Fouque, G. Papanicolaou and K.-R. Sircar : Derivatives in financial markets with stochastic volatility, Cambridge Univeristy Press, Cambridge, 2000.

N. Hilber, O. Reichmann, Ch. Schwab and Ch. Winter: Computational Methods for Quantitative Finance, Springer Finance, Springer, 2013.
Voraussetzungen / BesonderesStart of the lecture: WED, 28 Feb. 2018 (second week of the semester).
401-8902-00LComputational Economics and Finance (University of Zurich)
Findet dieses Semester nicht statt.
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: MFOEC167

Beachten Sie die Einschreibungstermine an der UZH: Link
W6 KP4VUni-Dozierende
KurzbeschreibungLearning to apply numerical methods for the computation of solutions of complex models in economics and finance.
LernzielOverview of the field "CEF"
LiteraturKenneth L. Judd, "Numerical Methods in Economics", ISBN: 0262100711
Voraussetzungen / BesonderesSolid knowledge of linear algebra and calculus.
401-8908-00LContinuous Time Quantitative Finance (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: MFOEC204

Beachten Sie die Einschreibungstermine an der UZH: Link
W3 KP3VUni-Dozierende
KurzbeschreibungAmerican Options, Stochastic Volatility, Lévy Processes and Option Pricing, Exotic Options, Transaction Costs and Real Options.
LernzielThe course focuses on the theoretical foundations of modern derivative pricing. It aims at deriving and explaining important option pricing models by relying on some mathematical tools of continuous time finance.
A particular focus on jump processes is given. The introduction of possible financial crashes is now essential in some models and a clear understanding of Poisson processes is therefore important. A standard background in stochastic calculus is required.
InhaltStochastic volatility models
Itô's formula and Girsanov theorem for jump-diffusion processes
The pricing of options in presence of possible discontinuities
Exotic options
Transaction costs
SkriptSee: Link
LiteraturSee: Link
Voraussetzungen / BesonderesThis course replaces "Continuous Time Quantitative Finance" (MFOEC108), which will be discontinued. Students who have taken "Continuous Time Quantitative Finance" (MFOEC108) in the past, are not allowed to book this course "Continuous Time Quantitative Finance" (MFOEC204).
401-5820-00LSeminar in Computational Finance for CSEW4 KP2SJ. Teichmann
Kurzbeschreibung
Lernziel
Electromagnetics
NummerTitelTypECTSUmfangDozierende
227-0662-00LOrganic and Nanostructured Optics and Electronics Information
Findet dieses Semester nicht statt.
W6 KP4GV. Wood
KurzbeschreibungThis course examines the optical and electronic properties of excitonic materials that can be leveraged to create thin-film light emitting devices and solar cells. Laboratory sessions provide students with experience in synthesis and optical characterization of nanomaterials as well as fabrication and characterization of thin film devices.
LernzielGain the knowledge and practical experience to begin research with organic or nanostructured materials and understand the key challenges in this rapidly emerging field.
Inhalt0-Dimensional Excitonic Materials (organic molecules and colloidal quantum dots)

Energy Levels and Excited States (singlet and triplet states, optical absorption and luminescence).

Excitonic and Polaronic Processes (charge transport, Dexter and Förster energy transfer, and exciton diffusion).

Devices (photodetectors, solar cells, and light emitting devices).
LiteraturLecture notes and reading assignments from current literature to be posted on website.
Voraussetzungen / BesonderesCourse grade will be based on a final project.
227-0707-00LOptimization Methods for EngineersW3 KP2GP. Leuchtmann
KurzbeschreibungErste Semesterhälfte: Einführung in die wichtigsten Methoden der numerischen Optimierung mit Schwerpunkt auf stochastische Verfahren wie genetische Algorithmen, evolutionäre Strategien, etc.
Zweite Semesterhälfte: Jeder Teilnehmer implementiert ein ausgewähltes Optimierungsverfahren und wendet es auf ein praktisches Problem an.
LernzielNumerische Optimierung spielt eine zunehmende Rolle sowohl bei der Entwicklung technischer Produkte als auch bei der Entwicklung numerischer Methoden. Die Studenten sollen lernen, geeignete Verfahren auszuwählen, weiter zu entwickeln und miteinander zu kombinieren um so praktische Probleme effizient zu lösen.
InhaltTypische Optimierungsprobleme und deren Tücken werden skizziert. Bekannte deterministische Suchalgorithmen, Verfahren der kombinatorische Minimierung und evolutionäre Algorithmen werden vorgestellt und miteinander verglichen. Da Optimierungsprobleme im Ingenieurbereich oft sehr komplex sind, werden Wege zur Entwicklung neuer, effizienter Verfahren aufgezeigt. Solche Verfahren basieren oft auf einer Verallgemeinerung oder einer Kombination von bekannten Verfahren. Zur Veranschaulichung werden aus dem breiten Anwendungsbereich numerischer Optimierungsverfahren verschiedenartigste praktische Probleme herausgegriffen
SkriptPDF of a short script (32 pages) plus the view graphs are provided
Voraussetzungen / BesonderesVorlesung 1. Semesterhälfte, Übungen in Form kleiner Projekte in der 2. Semesterhälfte, Präsentation der Resultate in der letzten Semesterwoche.
401-5870-00LSeminar in Electromagnetics for CSEW4 KP2SJ. Leuthold
KurzbeschreibungDiscussion of fundamentals of electromagnetics and various applications (wave propagation, scattering, antennas, waveguides, bandgap materials, etc.). Numerical methods suited for the analysis of electromagnetic fields and for the optimal design of electromagnetic structures.
LernzielKnowledge about classical electromagnetics, main applications, and appropriate numerical methods.
Voraussetzungen / BesonderesStudents study a selected topic and give a 15-30 minutes presentation towards the end of the semester. The topic and the supervisor is defined in a discussion with C. Hafner or J. Leuthold.
Geophysik
Empfohlene Kombinationen:
Fach 1 + Fach 2
Fach 1 + Fach 3
Fach 2 + Fach 3
Fach 3 + Fach 4
Fach 5 + Fach 6
Fach 5 + Fach 4
Geophysik: Fach 1
findet im Herbstsemester statt
Geophysik: Fach 2
findet im Herbstsemester statt
Geophysik: Fach 3
NummerTitelTypECTSUmfangDozierende
651-4008-00LDynamics of the Mantle and LithosphereW3 KP2GA. Rozel
KurzbeschreibungDas Ziel dieses Kurses ist, ein ausführliches Verständnis der physikalischen Eigenschaften, der Struktur und des dynamischen Verhaltens des Mantle-Lithosphäre Systems zu erreichen. Der Kurs fokussiert hauptsächlich auf die Erde aber bespricht auch wie diese Prozesse in anderen terrestrischen Planeten auftreten.
LernzielDas Ziel dieses Kurses ist, ein ausführliches Verständnis der physikalischen Eigenschaften, der Struktur und des dynamischen Verhaltens des Umhang-Lithoshäre Systems zu erreichen, konzentriert, hauptsächlich auf Masse aber auch bespricht, wie diese Prozesse anders als in anderen terrestrischen Planeten auftreten.
Geophysik: Fach 4
nur anrechenbar, falls beide Lerneinheiten erfolgreich abgeschlossen werden
NummerTitelTypECTSUmfangDozierende
651-4094-00LNumerical Modelling for Applied Geophysics IW3 KP2GJ. Robertsson
KurzbeschreibungThis course provides an introduction to numerical modelling techniques as they are employed in many projects in Applied Geophysics. The focus is rather on the basic principles and applications than on rigorous mathematical proofs. Prerequisites for this course include (i) basic knowledge of vector analysis and Fourier transform techniques and (ii) knowledge of Matlab (required for the exercises).
LernzielAfter this course the students should have a good overview of the numerical modelling techniques that are commonly applied in Applied Geophysics. They should be familiar with the basic principles of the methods. Furthermore, they should know advantages and disadvantages as well as the limitations of the individual approaches.
InhaltDuring the first part of the course, the following topics are covered:
- General issues about finite precision of numerical modeling
- Potential field modeling
- Layered Earth modeling using transform methods
- Finite differences
- Finite elements
- Other numerical methods

Most of these modules are accompanied by exercises

Small projects will be assigned to the students. They either include a programming exercise or applications of existing modelling codes.
SkriptPresentation slides and some background material will be provided.
Voraussetzungen / BesonderesThis course is offered as a half-semester course during the first part of the semester
651-4096-00LInverse Theory for Geophysics I: BasicsW3 KP2VA. Fichtner
KurzbeschreibungThis course provides an introduction to inversion theory. The focus is rather on the basic principles and applications than on rigorous mathematical proofs. Prerequisites for this course include (i) basic knowledge of analysis and linear algebra and (ii) knowledge of Matlab (required for the exercises).
LernzielAfter this course the students should have a good grasp of geophysical inversion problems. In particular, they should be familiar with linear and non-linear inversion techniques. Most importantly, they should be aware of potential pitfalls and limitations of the methods.
InhaltDuring this course, the following topics are covered:

- Introduction to geophysical inversion
- Matrix inversion techniques
- Linear inversion problems
- Non-linear inversion problems
- Probabilistic inversion approaches
- Global optimizers

Most of these modules are accompanied by exercises
SkriptPresentation slides and some background material will be provided.
Voraussetzungen / BesonderesThis course is offered as a half-semester course during the first part of the semester
Geophysik: Fach 5
findet im Herbstsemester statt
Geophysik: Fach 6
NummerTitelTypECTSUmfangDozierende
651-4006-00LSeismology of the Spherical EarthW3 KP2GM. van Driel, S. C. Stähler
KurzbeschreibungBrief review of continuum mechanics and the seismic wave equation; P and S waves; reciprocity and representation theorems; eikonal equation and ray tracing; Huygens and Fresnel; surface-waves; normal-modes; seismic interferometry and noise; numerical solutions.
LernzielAfter taking this course, students will have the background knowledge necessary to start an original research project in quantitative seismology.
LiteraturShearer, P., Introduction to Seismology, Cambridge University Press,
1999.

Aki, K. and P. G. Richards, Quantitative Seismology, second edition,
University Science Books, Sausalito, 2002.

Dahlen, F. A. and J. Tromp, Theoretical Global Seismology, Princeton
University Press, Princeton, 1998.

Lay, T. and T. C. Wallace, Modern Global Seismology, Academic Press,
San Diego, 1995.

Udias, A., Principles of Seismology, Cambridge University Press, 1999.
Voraussetzungen / BesonderesBasic knowledge in vector calculus and seismology (e.g. from the 'wave propagation' lecture).
Geophysik: Seminar
NummerTitelTypECTSUmfangDozierende
401-5880-00LSeminar in Geophysics for CSEW4 KP2SP. Tackley
Kurzbeschreibung
Lernziel
Biologie
NummerTitelTypECTSUmfangDozierende
636-0702-00LStatistical Models in Computational BiologyW6 KP2V + 1U + 2AN. Beerenwinkel
KurzbeschreibungThe course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods.
LernzielThe goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets.
InhaltGraphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises.
Skriptno
Literatur- Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252
- Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007.
- Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004
701-1708-00LInfectious Disease DynamicsW4 KP2VS. Bonhoeffer, R. D. Kouyos, R. R. Regös, T. Stadler
KurzbeschreibungThis course introduces into current research on the population biology of infectious diseases. The course discusses the most important mathematical tools and their application to relevant diseases of human, natural or managed populations.
LernzielAttendees will learn about:
* the impact of important infectious pathogens and their evolution on human, natural and managed populations
* the population biological impact of interventions such as treatment or vaccination
* the impact of population structure on disease transmission

Attendees will learn how:
* the emergence spread of infectious diseases is described mathematically
* the impact of interventions can be predicted and optimized with mathematical models
* population biological models are parameterized from empirical data
* genetic information can be used to infer the population biology of the infectious disease

The course will focus on how the formal methods ("how") can be used to derive biological insights about the host-pathogen system ("about").
InhaltAfter an introduction into the history of infectious diseases and epidemiology the course will discuss basic epidemiological models and the mathematical methods of their analysis. We will then discuss the population dynamical effects of intervention strategies such as vaccination and treatment. In the second part of the course we will introduce into more advanced topics such as the effect of spatial population structure, explicit contact structure, host heterogeneity, and stochasticity. In the final part of the course we will introduce basic concepts of phylogenetic analysis in the context of infectious diseases.
SkriptSlides and script of the lecture will be available online.
LiteraturThe course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Keeling & Rohani, Modeling Infectious Diseases in Humans and Animals, Princeton Univ Press 2008
* Anderson & May, Infectious Diseases in Humans, Oxford Univ Press 1990
* Murray, Mathematical Biology, Springer 2002/3
* Nowak & May, Virus Dynamics, Oxford Univ Press 2000
* Holmes, The Evolution and Emergence of RNA Viruses, Oxford Univ Press 2009
Voraussetzungen / BesonderesBasic knowledge of population dynamics and population genetics as well as linear algebra and analysis will be an advantage.
Wahlfächer
NummerTitelTypECTSUmfangDozierende
151-0834-00LUmformtechnik II - Numerische Simulationsverfahren Information W4 KP2V + 2UP. Hora
KurzbeschreibungVermitteln der Grundlagen der nichtlinearen Finite-Elemente-Methoden. Implizite und explizite FEM-Verfahren für quasistatische Anwendungen; Modellierung von thermo-mechanisch gekoppelten Problemen; Modellierung von zeitlich veränderlichen Kontaktbedingungen; Modellierung des nichtlinearen Werkstoffverhaltens; Modellierung der Reibung; FEM-basierte Voraussage von Versagen durch Risse und Falten.
LernzielProzessoptimierung durch Einsatz numerischer Verfahren.
InhaltEinsatz virtueller Simulationsmethoden zur Planung und Optimierung von Umformprozessen. Grundlagen der virtuellen Simulationsverfahren, basierend auf der Methode der Finiten Elemente (FEM) und der Methode der Finiten Differenzen (FDM). Einführung in die Grundlagen der Kontinuums- und Plastomechanik zur mathematischen Beschreibung des plastischen Werkstoffflusses bei Metallen. Vorgehensweisen bei der Ermittlung prozessrelevanter Kenndaten. Uebnungen: Einsatz industrieller Simulationspakete für die Anwendungen Tiefziehen (Automotive), Innenhochdruckumformen (Space-Frame) und Strangpressen.
Skriptja
151-0836-00LMethoden der virtuellen Prozessauslegung umformtechnischer Systeme Information W5 KP2V + 2UP. Hora
KurzbeschreibungEinführung in die heutigen Möglichkeiten der digitalen Fabrikmodellierung mit Beispielen aus den Bereichen digitale Automobilfabrik, digitale IHU-Fabrik, digitale Strangpressfabrik. Vermittelt werden Methoden der nicht-linearen FEM-Prozessanalyse, der nicht-linearen Optimierung und der stochastischen Prozesssimulation für umformtechnische Anwendungen.
LernzielVertiefter Einsatz virtueller Planungstools zur Kontrolle und Auslegung von umformtechnischen Fertigungsverfahren.
InhaltEinführung in die heutigen Möglichkeiten der digitalen Fabrikmodellierungen. Fallstudien: digitale Automobilfabrik, digitalen IHU-Fabrik, digitale Strangpressfabrik. Prozessschritte: Virtuelle Auslegung der Prozesse, tryout der Werkzeuge, Untersuchung der Parametersensitivität. Mathematische Methoden: nicht-lineare FEM, Methoden der nicht-linearen Optimierung, stochastische Verfahren zur Robustheitsuntersuchung.
Skriptja
151-3202-00LProduct Development and Engineering Design Belegung eingeschränkt - Details anzeigen
Number of participants limited to 70.
W4 KP2GK. Shea, T. Stankovic
KurzbeschreibungThe course introduces students to the product development process. In a team, you will explore the early phases of conceptual development and product design, from ideation and concept generation through to hands-on prototyping. This is an opportunity to gain product development experience and improve your skills in prototyping and presenting your product ideas. The project topic changes each year.
LernzielThe course introduces you to the product development process and methods in engineering design for: product planning, user-centered design, creating product specifications, ideation including concept generation and selection methods, material selection methods and prototyping. Further topics include product lifecycle and sustainable design as well as design for manufacture, focusing on additive manufacture. You will actively apply the process and methods learned throughout the semester in a team on a product development project including hands-on prototyping.
InhaltWeekly topics accompanying the product development project include:
1 Introduction to Product Development and Engineering Design
2 Product Planning and Social-Economic-Technology (SET) Factors
3 User-Centered Design and Product Specification
4 Concept Generation and Selection Methods
5 System Design and Embodiment Design
6 Hands-On Prototyping and Prototype Planning
7 Material Selection in Engineering Design
8 Product Lifecycle and Sustainability
9 Design for Manufacture and Design for Additive Manufacture
Skriptavailable on Moodle
LiteraturUlrich and Eppinger, Product Design and Development, 6th Edition, McGraw Hill Education, 2016.

Cagan and Vogel, Creating Breakthrough Products: Revealing the Secrets that Drive Global Innovation, 2nd Edition, Pearson Education, 2013.
Voraussetzungen / BesonderesAlthough the course is offered to ME (BSc and MSc) and CS (BSc and MSc) students, priority will be given to ME BSc students in the Focus Design, Mechanics, and Materials if the course is full.
151-0840-00LPrinciples of FEM-Based Optimization and Robustness Analysis Information W5 KP2V + 2UB. Berisha, P. Hora, N. Manopulo
KurzbeschreibungDie Vorlesung vermittelt Grundlagen im Bereich stochastischer Simulationen und nichtlinearer Optimierungsmethoden. Zuerst werden die Methoden der nichtlinearen Optimierung für komplexe mechanische Systeme hergleitet und anschliessend auf reale Prozesse angewendet. Typische Anwendungen von stochastischen Methoden zur Vorhersage von Prozessstabilität und Robustheitsbewertungen werden behandelt.
LernzielIm Allgemeinen sind reale Systeme nichtlinear. Desweiteren unterliegen reale Prozesse Prozessschwankungen. Trotzdem werden gewöhnlich bei der Simulation zufallsunabhängige Randbedingungen mit konstanten Parametern angenommen. Demzufolge können mit diesen Ergebnissen keine Rückschlüsse auf das reale Systemverhalten gezogen werdnen. Das Ziel dieser Vorlesung ist es, einen Einblick in die Methoden der stochastischen Simulation und der nichtlinearen Optimierung zu geben.

Der Student lernt mathematische Methoden wie bspw. gradientenbasierte und gradientenfreie Methoden (Genetische Algorithmen) kennen. Er lernt den Umgang mit Optimierungsprogrammen (Matlab Optimization Toolbox) und löst damit grundlegende Probleme im Bereich Optimierung und Stochastik.

Desweiteren wird besonders auf die Optimierung und Robustheitsuntersuchungen von Ingenieursproblemen, unter Anwendung von kommerzieller Finite Elemente Software wie LS-Dyna und Optimierungssoftware wie LS-Opt, eingegangen.
InhaltGrundlagen der nichtlinearen Optimierung

- Einführung in die Problematik der nichtlinearen Optimierung und der stochastischen Prozesssimulation
- Grundlagen der nichtlinearen Optimierung
- Einführung in LS-Opt
- Design of Experiments DoE
- Einführung in die nichtlineare FEM

Optimierung nichtlinearer Systeme

- Anwendungsfall: Optimierung einfacher Tragwerke (LS-Dyna, LS-Opt)
- Optimierung mittels Metamodellen
- Einführung in die Strukturoptimierung
- Einführung in die Geometriparametrisierung zur Formoptimierung

Robustheit und Sensitivität mehrparametriger Systeme

- Einführung in die Stochastik und Robustheit von Prozessen
- Sensitivitätsanalysen
- Anwendungsbeispiele
Skriptja
151-0206-00LEnergy Systems and Power EngineeringW4 KP2V + 2UR. S. Abhari, A. Steinfeld
KurzbeschreibungIntroductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.
LernzielIntroductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.
InhaltWorld primary energy resources and use: fossil fuels, renewable energies, nuclear energy; present situation, trends, and future developments. Sustainable energy system and environmental impact of energy conversion and use: energy, economy and society. Electric power and the electricity economy worldwide and in Switzerland; production, consumption, alternatives. The electric power distribution system. Renewable energy and power: available techniques and their potential. Cost of electricity. Conventional power plants and their cycles; state-of-the -art and advanced cycles. Combined cycles and cogeneration; environmental benefits. Solar thermal power generation and solar photovoltaics. Hydrogen as energy carrier. Fuel cells: characteristics, fuel reforming and combined cycles. Nuclear power plant technology.
SkriptVorlesungsunterlagen werden verteilt
151-0306-00LVisualization, Simulation and Interaction - Virtual Reality I Information W4 KP4GA. Kunz
KurzbeschreibungTechnologie der virtuellen Realität. Menschliche Faktoren, Erzeugung virtueller Welten, Beleuchtungsmodelle, Display- und Beschallungssysteme, Tracking, haptische/taktile Interaktion, Motion Platforms, virtuelle Prototypen, Datenaustausch, VR-Komplettsysteme, Augmented Reality; Kollaborationssysteme; VR und Design; Umsetzung der VR in der Industrie; Human COmputer Interfaces (HCI).
LernzielDie Studierenden erhalten einen Überblick über die virtuelle Realität, sowohl aus technischer als auch aus informationstechnologischer Sicht. Sie lernen unterschiedliche Software- und Hardwareelemente kennen sowie deren Einsatzmöglichkeiten im Geschäftsprozess. Die Studierenden entwickeln eine Kenntnis darüber, wo sich heute die virtuelle Realität nutzbringend einsetzen lässt und wo noch weiterer Forschungsbedarf besteht. Anhand konkreter Programme und Systeme erfahren die Teilnehmer den Umgang mit den erlernten neuen Technologien.
InhaltDiese Vorlesung gibt eine Einführung in die Technologie der virtuellen Realität als neues Tool zur Bewältigung komplexer Geschäftsprozesse. Es sind die folgenden Themen vorgesehen: Einführung und Geschichte der VR; Eingliederung der VR in die Produktentwicklung; Nutzen von VR für die Industrie; menschliche Faktoren als Grundlage der virtuellen Realität; Einführung in die Erzeugung (Modellierung) virtueller Welten; Beleuchtungsmodelle; Kollisionserkennung; Displaysysteme; Projektionssysteme; Beschallungssysteme; Trackingssysteme; Interaktionsgeräte für die virtuelle Umgebung; haptische und taktile Interaktion; Motion Platforms; Datenhandschuh; physikalisch basierte Simulation; virtuelle Prototypen; Datenaustausch und Datenkommunikation; VR-Komplettsysteme; Augmented Reality; Kollaborationssysteme; VR zur Unterstützung von Designaufgaben; Umsetzung der VR in der Industrie; Ausblick in die laufende Forschung im Bereich VR.

Lehrmodule:
- Geschichte der VR und Definition der wichtigsten Begriffe
- Einordnung der VR in Geschäftsprozesse
- Die Erzeugung virtueller Welten
- Geräte und Technologien für die immersive virtuelle Realität
- Anwendungen der VR in unterschiedlichsten Gebieten
SkriptDie Durchführung der Lehrveranstaltung erfolgt gemischt mit Vorlesungs- und Übungsanteilen.
Die Vorlesung kann auf Wunsch in Englisch erfolgen. Das Skript ist ebenfalls in Englisch verfügbar.
Skript, Handout; Kosten SFr.50.-
Voraussetzungen / BesonderesVoraussetzungen:
keine
Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF

Testat/ Kredit-Bedingungen/ Prüfung:
– Teilnahme an Vorlesung und Kolloquien
– Erfolgreiche Durchführung von Übungen in Teams
– Mündliche Einzelprüfung 30 Minuten
151-0314-00LInformationstechnologien im digitalen ProduktW4 KP3GE. Zwicker, R. Montau
KurzbeschreibungZielsetzung, Methoden und Konzepte Digitales Produkt und Product-Life-Cycle-Management (PLM)
Grundlagen Digitales Produkt: Produktstrukturierung, Optimierung Entwicklungs- und Engineeringprozess, Verteilung und Nutzung von Produktdaten in Verkauf, Produktion / Montage, Service
PLM-Grundlagen: Objekte, Strukturen, Prozesse, Integrationen
Praktische Anwendung.
LernzielDie Studierenden lernen vertieft die Grundlagen und Konzepte des Produkt-Lifecycle-Management (PLM), den Einsatz von Datenbanken, die Integration von CAx-Systemen, den Aufbau von Computer-Netzwerken und deren Protokolle, moderne computerunterstützte Kommunikation (CSCW) oder das Varianten- und Konfigurationsmanagement im Hinblick auf die Erstellung, Verwaltung und Nutzung des Digitalen Produktes.
InhaltMöglichkeiten und Potentiale der Nutzung moderner IT-Tools, insbesondere moderner CAx- und PLM- Technologien. Der zielgerichtete Einsatz von CAx- und PLM-Technologien im Zusammenhang Produkt-Plattform - Unternehmensprozesse - IT-Tools. Einführung in die Konzepte des Produkt-Lifecycle-Managements (PLM): Informationsmodellierung, Verwaltung, Revisionierung, Kontrolle und Verteilung von Produktdaten bzw. Produkt-Plattformen. Detaillierter Aufbau und Funktionsweise von PLM-Systemen. Integration neuer IT-Technologien in bestehende und neu zu strukturierende Unternehmensprozesse. Möglichkeiten der Publikation und der automatischen Konfiguration von Produktvarianten auf dem Internet. Einsatz modernster Informations- und Kommunikationstechnologien (CSCW) beim Entwickeln von Produkten durch global verteilte Entwicklungszentren. Schnittstellen der rechnerintegrierten und unternehmensübergreifenden Produktentwicklung. Auswahl und Projektierung, Anpassung und Einführung von PLM-Systemen. Beispiele und Fallstudien für den industriellen Einsatz moderner Informationstechnologien.

Lehrmodule
- Einführung in die PLM-Technologie
- Datenbanktechnologie im Digitalen Produkt
- Objektmanagement
- Objektklassifikation
- Objektidentifikation mit Sachnummernsystem
- Prozess- Kooperationsmanagement
- Workflow Management
- Schnittstellen im Digitalen Produkt
- Enterprises Application Integration
SkriptDidaktisches Konzept/ Unterlagen/ Kosten
Die Durchführung der Lehrveranstaltung erfolgt gemischt mit Vorlesungs- und Übungsanteilen anhand von Praxisbeispielen.
Handouts für Inhalt und Case; zT. E-learning; Kosten Fr.20.--
Voraussetzungen / BesonderesVoraussetzungen
Empfohlen:
Informatik II; Fokus-Projekt; Freude an Informationstechnologien

Testat/ Kredit-Bedingungen / Prüfung
Erfolgreiche Durchführung von Übungen in Teams
Mündliche Prüfung 30 Minuten, theoretisch und anhand konkreter Problemstellungen
151-0361-00LAn Introduction to the Finite-Element MethodW4 KP3GG. Kress, C. Thurnherr
KurzbeschreibungThe class includes mathematical ancillary concepts, derivation of element equations, numerical integration, boundary conditions and degree-of-freedom coupling, compilation of the system’s equations, element technology, solution methods, static and eigenvalue problems, iterative solution of progressing damage, beam-locking effect, modeling techniques, implementation of nonlinear solution methods.
LernzielObtain a theoretical background of the finite-element method.
Understand techniques for finding numerically more efficient finite elements. Understand degree-of-freedom coupling schemes and recall typical equations solution algorithms for static and eigenvalue problems. Learn how to map specific mechanical situations correctly to finite-element models. Understand how to make best use of FEM for structural analysis. Obtain a first inside into the implementation of nonlinear FEM procedures.
Inhalt1. Introduction, direct element derivation of truss element
2. Variational methods and truss element revisited
3. Variational methods and derivation of planar finite elements
4. Curvilinear finite elements and numerical integration
5. Element Technology
6. Degrees-of-freedom coupling and solution methods
7. Iterative solution methods for damage progression analysis
8. Shear-rigid and shear compliant beam elements and locking effect
9. Beam Elements and Locking Effect
10. Harmonic vibrations and vector iteration
11. Modeling techniques
12. Implementation of nonlinear FEM procedures
SkriptScript and handouts are provided in class and can also be down-loaded from:
Link
LiteraturNo textbooks required.
151-0660-00LModel Predictive Control Information W4 KP2V + 1UM. Zeilinger
KurzbeschreibungModel predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.
LernzielDesign and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Inhalt- Review of required optimal control theory
- Basics on optimization
- Receding-horizon control (MPC) for constrained linear systems
- Theoretical properties of MPC: Constraint satisfaction and stability
- Computation: Explicit and online MPC
- Practical issues: Tracking and offset-free control of constrained systems, soft constraints
- Robust MPC: Robust constraint satisfaction
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
SkriptScript / lecture notes will be provided.
Voraussetzungen / BesonderesOne semester course on automatic control, Matlab, linear algebra.
Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.

Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
151-0940-00LModelling and Mathematical Methods in Process and Chemical EngineeringW4 KP3GM. Mazzotti
KurzbeschreibungEinführung in die Modellierungstechniken und mathematischen Methoden für nichtnumerische Lösungen von Gleichungen in der chemischen Verfahrenstechnik.
LernzielEinführung in die Modellierungstechniken und mathematischen Methoden für nichtnumerische Lösungen von Gleichungen in der chemischen Verfahrenstechnik.
InhaltFormulierung und Bearbeitung von mathematischen Modellen, Auswertung und Präsentation von Resultaten, Matrizen und deren Anwendung, Nichtlineare, gewöhnliche Differentialgl. erster Ordnung u. Stabilitätstheorem, Partielle Differenzialgleichungen erster Ordnung, Einführung in die Störungstheorie, Fallstudien: Mehrdeutigkeiten und Stabilität eines kontinuierlichen Rührkessels; Rückstandskurvendiagramme für einfache Destillation; Dynamik von Chromatographiekolonnen; Kinetik und Dynamik von oszillierenden Reaktionen.
Skriptkein Skript
LiteraturA. Varma, M. Morbidelli, "Mathematical methods in chemical engineering," Oxford University Press (1997)
H.K. Rhee, R. Aris, N.R. Amundson, "First-order partial differential equations. Vol. 1," Dover Publications, New York (1986)
R. Aris, "Mathematical modeling: A chemical engineer’s perspective," Academic Press, San Diego (1999)
151-0980-00LBiofluiddynamicsW4 KP2V + 1UD. Obrist, P. Jenny
KurzbeschreibungIntroduction to the fluid dynamics of the human body and the modeling of physiological flow processes (biomedical fluid dynamics).
LernzielA basic understanding of fluid dynamical processes in the human body. Knowledge of the basic concepts of fluid dynamics and the ability to apply these concepts appropriately.
InhaltThis lecture is an introduction to the fluid dynamics of the human body (biomedical fluid dynamics). For selected topics of human physiology, we introduce fundamental concepts of fluid dynamics (e.g., creeping flow, incompressible flow, flow in porous media, flow with particles, fluid-structure interaction) and use them to model physiological flow processes. The list of studied topics includes the cardiovascular system and related diseases, blood rheology, microcirculation, respiratory fluid dynamics and fluid dynamics of the inner ear.
SkriptLecture notes are provided electronically.
LiteraturA list of books on selected topics of biofluiddynamics can be found on the course web page.
101-0178-01LUncertainty Quantification in Engineering Information W3 KP2GB. Sudret, S. Marelli
KurzbeschreibungUncertainty quantification aims at studying the impact of aleatory and epistemic uncertainty onto computational models used in science and engineering. The course introduces the basic concepts of uncertainty quantification: probabilistic modelling of data (copula theory), uncertainty propagation techniques (Monte Carlo simulation, polynomial chaos expansions), and sensitivity analysis.
LernzielAfter this course students will be able to properly pose an uncertainty quantification problem, select the appropriate computational methods and interpret the results in meaningful statements for field scientists, engineers and decision makers. The course is suitable for any master/Ph.D. student in engineering or natural sciences, physics, mathematics, computer science with a basic knowledge in probability theory.
InhaltThe course introduces uncertainty quantification through a set of practical case studies that come from civil, mechanical, nuclear and electrical engineering, from which a general framework is introduced. The course in then divided into three blocks: probabilistic modelling (introduction to copula theory), uncertainty propagation (Monte Carlo simulation and polynomial chaos expansions) and sensitivity analysis (correlation measures, Sobol' indices). Each block contains lectures and tutorials using Matlab and the in-house software UQLab (Link).
SkriptDetailed slides are provided for each lecture. A printed script gathering all the lecture slides may be bought at the beginning of the semester.
Voraussetzungen / BesonderesA basic background in probability theory and statistics (bachelor level) is required. A summary of useful notions will be handed out at the beginning of the course.

A good knowledge of Matlab is required to participate in the tutorials and for the mini-project.
227-0418-00LAlgebra and Error Correcting Codes Information W6 KP4GH.‑A. Loeliger
KurzbeschreibungThe course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
LernzielThe course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
InhaltError correcting codes: coding and modulation, linear codes, Hamming space codes, Euclidean space codes, trellises and Viterbi decoding, convolutional codes, factor graphs and message passing algorithms, low-density parity check codes, turbo codes, polar codes, Reed-Solomon codes.

Algebra: groups, rings, homomorphisms, quotient groups, ideals, finite fields, vector spaces, polynomials.
SkriptLecture Notes (english)
227-0420-00LInformation Theory II Information
Findet dieses Semester nicht statt.
W6 KP2V + 2UA. Lapidoth
KurzbeschreibungThis course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.
LernzielThe course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory.
InhaltDifferential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem.
Skriptn/a
LiteraturT.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006
227-0434-10LMathematics of InformationW8 KP3V + 2U + 2AH. Bölcskei
KurzbeschreibungThe class focuses on fundamental mathematical aspects of data sciences: Information theory (lossless and lossy compression), sampling theory, compressed sensing, dimensionality reduction (Johnson-Lindenstrauss Lemma), randomized algorithms for large-scale numerical linear algebra, approximation theory, neural networks as function approximators, mathematical foundations of deep learning.
LernzielAfter attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the most commonly used mathematical theories in data science. Students will also have to carry out a research project, either individually or in groups, with presentations at the end of the semester.
Inhalt1. Information theory: Entropy, mutual information, lossy compression, rate-distortion theory, lossless compression, arithmetic coding, Lempel-Ziv compression

2. Signal representations: Frames in finite-dimensional spaces, frames in Hilbert spaces, wavelets, Gabor expansions

3. Sampling theorems: The sampling theorem as a frame expansion, irregular sampling, multi-band sampling, density theorems, spectrum-blind sampling

4. Sparsity and compressed sensing: Uncertainty principles, recovery algorithms, Lasso, matching pursuits, compressed sensing, non-linear approximation, best k-term approximation, super-resolution

5. High-dimensional data and dimensionality reduction: Random projections, the Johnson-Lindenstrauss Lemma, sketching

6. Randomized algorithms for large-scale numerical linear algebra: Large-scale matrix computations, randomized algorithms for approximate matrix factorizations, matrix sketching, fast algorithms for large-scale FFTs

7. Mathematics of (deep) neural networks: Universal function approximation with single-and multi-layer networks, fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, geometry of decision surfaces, convolutional neural networks, scattering networks
SkriptDetailed lecture notes will be provided as we go along.
Voraussetzungen / BesonderesThis course is aimed at students with a background in basic linear algebra, analysis, and probability. We will, however, review required mathematical basics throughout the semester in the exercise sessions.
227-0104-00LCommunication and Detection Theory Information W6 KP4GA. Lapidoth
KurzbeschreibungThis course teaches the foundations of modern digital communications and detection theory. Topics include the geometry of the space of energy-limited signals; the baseband representation of passband signals, spectral efficiency and the Nyquist Criterion; the power and power spectral density of PAM and QAM; hypothesis testing; Gaussian stochastic processes; and detection in white Gaussian noise.
LernzielThis is an introductory class to the field of wired and wireless communication. It offers a glimpse at classical analog modulation (AM, FM), but mainly focuses on aspects of modern digital communication, including modulation schemes, spectral efficiency, power budget analysis, block and convolu- tional codes, receiver design, and multi- accessing schemes such as TDMA, FDMA and Spread Spectrum.
Inhalt- Baseband representation of passband signals.
- Bandwidth and inner products in baseband and passband.
- The geometry of the space of energy-limited signals.
- The Sampling Theorem as an orthonormal expansion.
- Sampling passband signals.
- Pulse Amplitude Modulation (PAM): energy, power, and power spectral density.
- Nyquist Pulses.
- Quadrature Amplitude Modulation (QAM).
- Hypothesis testing.
- The Bhattacharyya Bound.
- The multivariate Gaussian distribution
- Gaussian stochastic processes.
- Detection in white Gaussian noise.
Skriptn/a
LiteraturA. Lapidoth, A Foundation in Digital Communication, Cambridge University Press, 2nd edition (2017)
227-0120-00LCommunication Networks Information W6 KP4GL. Vanbever
KurzbeschreibungThe students will understand the fundamental concepts of communication networks, with a focus on computer networking. They will learn to identify relevant mechanisms that are used in networks, and will see a reasonable set of examples implementing such mechanisms, both as seen from an abstract perspective and with hands-on, practical experience.
LernzielThe students will understand the fundamental concepts of communication networks, with a focus on computer networking. They will learn to identify relevant mechanisms that are used to networks work, and will see a reasonable set of examples implementing such mechanisms, both as seen from an abstract perspective and with hands-on, practical experience.
SkriptLecture notes and material for the course will be available before each course on: Link
Voraussetzungen / BesonderesPrerequisites: A layered model of communication systems (represented by the OSI Reference Model) has previously been introduced.
227-0158-00LSemiconductor Devices: Transport Theory and Monte Carlo Simulation Information
Findet dieses Semester nicht statt.
W4 KP2V + 1U
KurzbeschreibungThe first part deals with semiconductor transport theory including the necessary quantum mechanics.
In the second part, the Boltzmann equation is solved with the stochastic methods of Monte Carlo simulation.
The exercises address also TCAD simulations of MOSFETs. Thus the topics include theoretical physics,
numerics and practical applications.
LernzielOn the one hand, the link between microscopic physics and its concrete application in device simulation is established; on the other hand, emphasis is also laid on the presentation of the numerical techniques involved.
InhaltQuantum theoretical foundations I (state vectors, Schroedinger and Heisenberg picture). Band structure (Bloch theorem, one dimensional periodic potential, density of states). Pseudopotential theory (crystal symmetries, reciprocal lattice, Brillouin zone).
Semiclassical transport theory (Boltzmann transport equation (BTE), scattering processes, linear transport).<br>
Monte Carlo method (Monte Carlo simulation as solution method of the BTE, algorithm, expectation values).<br>
Implementational aspects of the Monte Carlo algorithm (discretization of the Brillouin zone, self-scattering according to Rees, acceptance- rejection method etc.). Bulk Monte Carlo simulation (velocity-field characteristics, particle generation, energy distributions, transport parameters). Monte Carlo device simulation (ohmic boundary conditions, MOSFET simulation).
Quantum theoretical foundations II (limits of semiclassical transport theory, quantum mechanical derivation of the BTE, Markov-Limes).
SkriptLecture notes (in German)
227-0159-00LSemiconductor Devices: Quantum Transport at the Nanoscale Information W6 KP2V + 2UM. Luisier, A. Emboras
KurzbeschreibungThis class offers an introduction into quantum transport theory, a rigorous approach to electron transport at the nanoscale. It covers different topics such as bandstructure, Wave Function and Non-equilibrium Green's Function formalisms, and electron interactions with their environment. Matlab exercises accompany the lectures where students learn how to develop their own transport simulator.
LernzielThe continuous scaling of electronic devices has given rise to structures whose dimensions do not exceed a few atomic layers. At this size, electrons do not behave as particle any more, but as propagating waves and the classical representation of electron transport as the sum of drift-diffusion processes fails. The purpose of this class is to explore and understand the displacement of electrons through nanoscale device structures based on state-of-the-art quantum transport methods and to get familiar with the underlying equations by developing his own nanoelectronic device simulator.
InhaltThe following topics will be addressed:
- Introduction to quantum transport modeling
- Bandstructure representation and effective mass approximation
- Open vs closed boundary conditions to the Schrödinger equation
- Comparison of the Wave Function and Non-equilibrium Green's Function formalisms as solution to the Schrödinger equation
- Self-consistent Schödinger-Poisson simulations
- Quantum transport simulations of resonant tunneling diodes and quantum well nano-transistors
- Top-of-the-barrier simulation approach to nano-transistor
- Electron interactions with their environment (phonon, roughness, impurity,...)
- Multi-band transport models
SkriptLecture slides are distributed every week and can be found at
Link
LiteraturRecommended textbook: "Electronic Transport in Mesoscopic Systems", Supriyo Datta, Cambridge Studies in Semiconductor Physics and Microelectronic Engineering, 1997
Voraussetzungen / BesonderesBasic knowledge of semiconductor device physics and quantum mechanics
227-0558-00LPrinciples of Distributed Computing Information W6 KP2V + 2U + 1AR. Wattenhofer, M. Ghaffari
KurzbeschreibungWe study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.
LernzielDistributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week.
InhaltDistributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems.

Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds
SkriptAvailable. Our course script is used at dozens of other universities around the world.
LiteraturLecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world.

Distributed Computing: Fundamentals, Simulations and Advanced Topics
Hagit Attiya, Jennifer Welch.
McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6

Introduction to Algorithms
Thomas Cormen, Charles Leiserson, Ronald Rivest.
The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8

Disseminatin of Information in Communication Networks
Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger.
Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2

Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes
Frank Thomson Leighton.
Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1

Distributed Computing: A Locality-Sensitive Approach
David Peleg.
Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8
Voraussetzungen / BesonderesCourse pre-requisites: Interest in algorithmic problems. (No particular course needed.)
252-0211-00LInformation Security Information W8 KP4V + 3UD. Basin, S. Capkun
KurzbeschreibungThis course provides an introduction to Information Security. The focus
is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
LernzielMaster fundamental concepts in Information Security and their
application to system building. (See objectives listed below for more details).
Inhalt1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to
defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic
cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric
encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and
variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design,
Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility
252-0407-00LCryptography Foundations Information W7 KP3V + 2U + 1AU. Maurer
KurzbeschreibungFundamentals and applications of cryptography. Cryptography as a mathematical discipline: reductions, constructive cryptography paradigm, security proofs. The discussed primitives include cryptographic functions, pseudo-randomness, symmetric encryption and authentication, public-key encryption, key agreement, and digital signature schemes. Selected cryptanalytic techniques.
LernzielThe goals are:
(1) understand the basic theoretical concepts and scientific thinking in cryptography;
(2) understand and apply some core cryptographic techniques and security proof methods;
(3) be prepared and motivated to access the scientific literature and attend specialized courses in cryptography.
InhaltSee course description.
Skriptyes.
Voraussetzungen / BesonderesFamiliarity with the basic cryptographic concepts as treated for
example in the course "Information Security" is required but can
in principle also be acquired in parallel to attending the course.
252-0526-00LStatistical Learning Theory Information W6 KP2V + 3PJ. M. Buhmann
KurzbeschreibungThe course covers advanced methods of statistical learning :
Statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
LernzielThe course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.
Inhalt# Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come.

# Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include:

* Maximum Entropy
* Information Bottleneck
* Deterministic Annealing

# Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures.

# Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike.

# Statistical physics models: approaches for large systems approximate optimization, which originate in the statistical physics (free energy minimization applied to spin glasses and other models); sampling methods based on these models
SkriptA draft of a script will be provided;
transparencies of the lectures will be made available.
LiteraturHastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.

L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Voraussetzungen / BesonderesRequirements:

knowledge of the Machine Learning course
basic knowledge of statistics, interest in statistical methods.

It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Statistical Learning Theory can be followed without the introductory course.
252-3005-00LNatural Language Understanding Information W4 KP2V + 1UT. Hofmann, M. Ciaramita
KurzbeschreibungThis course presents topics in natural language processing with an emphasis on modern techniques, primarily focusing on statistical and deep learning approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
LernzielThe objective of the course is to learn the basic concepts in the statistical processing of natural languages. The course will be project-oriented so that the students can also gain hands-on experience with state-of-the-art tools and techniques.
InhaltThis course presents an introduction to general topics and techniques used in natural language processing today, primarily focusing on statistical approaches. The course provides an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems.
LiteraturLectures will make use of textbooks such as the one by Jurafsky and Martin where appropriate, but will also make use of original research and survey papers.
263-0008-00LComputational Intelligence Lab
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
W8 KP2V + 2U + 1AT. Hofmann
KurzbeschreibungThis laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications.
LernzielStudents acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems that solve real-world problems. They learn to successfully develop solutions to application problems by following the key steps of modeling, algorithm design, implementation and experimental validation.

This lab course has a strong focus on practical assignments. Students work in groups of two to three people, to develop solutions to three application problems: 1. Collaborative filtering and recommender systems, 2. Text sentiment classification, and 3. Road segmentation in aerial imagery.

For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper.

(Disclaimer: The offered projects may be subject to change from year to year.)
Inhaltsee course description
252-0570-00LGame Programming Laboratory Information
Im Masterstudium können zusätzlich zu den Vertiefungsübergreifenden Fächern nur max. 10 Kreditpunkte über Laboratorien erarbeitet werden. Weitere Laboratorien werden auf dem Beiblatt aufgeführt.
W10 KP9PB. Sumner
KurzbeschreibungDas Ziel dieses Kurses ist ein vertieftes Verständnis der Technologie und der Programmierung von Computer-Spielen. Die Studierenden entwerfen und entwickeln in kleinen Gruppen ein Computer-Spiel und machen sich so vertraut mit der Kunst des Spiel-Programmierens.
LernzielDas Ziel dieses neuen Kurses ist es, die Studenten mit der Technologie und der Kunst des Programmierens von modernen dreidimensionalen Computerspielen vertraut zu machen.
InhaltDies ist ein neuer Kurs, der auf die Technologie von modernen dreidimensionalen Computerspielen eingeht. Während des Kurses werden die Studenten in kleinen Gruppen ein Computerspiel entwerfen und entwickeln. Der Schwerpunkt des Kurses wird auf technischen Aspekten der Spielentwicklung wie Rendering, Kinematographie, Interaktion, Physik, Animation und KI liegen. Zusätzlich werden wir aber auch Wert auf kreative Ideen für fortgeschrittenes Gameplay und visuelle Effekte legen.

Der Kurs wird als „Labor“ durchgeführt. Anstelle von traditionellen Vorträgen und Übungen wird der Kurs in einen praktischen, hands-on Ansatz durchgeführt. Wir treffen uns einmal wöchentlich um technische Aspekte zu besprechen und den Fortschritt der Entwicklung zu verfolgen. Wir planen das XNA Game Studio Express von Microsoft zu verwenden, eine Ansammlung von Bibliotheken und Werkzeugen um die Spieleentwicklung zu erleichtern. Die Entwicklung wird zunächst auf dem PC stattfinden, das Spiel wird dann im weiteren Verlauf auf der Xbox 360 Konsole eingesetzt.

Am Ende des Kurses werden die Resultate öffentlich präsentiert.
SkriptOnline XNA Dokumentation.
Voraussetzungen / BesonderesDie Anzahl der Teilnehmer wird begrenzt sein.

Voraussetzung für die Teilnahme sind:

- Gute Programmierkenntnisse (Java, C++, C#, o.ä.)

- Erfahrung in Computergrafik: Teilnehmer sollten mindestens die Vorlesung Visual Computing besucht haben. Wir empfehlen auch noch die weiterführenden Kurse Introduction to Computer Graphics, Surface Representations and Geometric Modeling, und Physically-based Simulation in Computer Graphics.
252-0504-00LNumerical Methods for Solving Large Scale Eigenvalue Problems Information
Der Kurs wird zum letzten Mal angeboten.
W4 KP3GP. Arbenz
KurzbeschreibungDie Vorlesung behandelt Algorithmen zur Lösung von Eigenwertproblemen
mit grossen, schwach besetzten Matrizen. Die z.T. erst in den letzten Jahren
entwickelten Verfahren werden theoretisch und praktisch mit MATLAB
untersucht.
LernzielKenntnisse der modernen Eigenlöser, ihres numerischen Verhaltens, ihrer Einsatzmöglichkeiten und Grenzen.
InhaltDie Vorlesung beginnt mit verschiedenartigen Beispielen für Anwendungen
in denen Eigenwertprobleme eine wichtige Rolle spielen. Nach einer
Einführung in die Lineare Algebra der Eigenwertprobleme wird ein
Überblick über Verfahren (QR-Algorithmus u.ä.) zur Behandlung kleiner
und mittelgrosser Eigenwertprobleme gegeben.

Danach werden die heute wichtigsten Löser für grosse, typischerweise
schwach-besetzte Matrixeigenwertprobleme vorgestellt und analysiert.
Dabei wird eine Auswahl der folgenden Themen behandelt:

* Vektor- und Teilraumiteration
* Spurminimierungsalgorithmus
* Arnoldi- und Lanczos-Algorithmus (inkl. Varianten mit Neustart)
* Davidson- und Jacobi-Davidson-Algorithmus
* vorkonditionierte inverse Iteration und LOBPCG
* Verfahren für nichtlineaere Eigenwertprobleme

In den Übungen werden diese Algorithmen (in vereinfachter Form) in
MATLAB implementiert und numerisch untersucht.
SkriptLecture notes (Englisch),
Kopien der Folien
LiteraturZ. Bai, J. Demmel, J. Dongarra, A. Ruhe, and H. van der Vorst: Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide. SIAM, Philadelphia, 2000.

Y. Saad: Numerical Methods for Large Eigenvalue Problems. Manchester University Press, Manchester, 1994.

G. H. Golub and Ch. van Loan: Matrix Computations, 3rd ed. Johns Hopkins University Press, Baltimore 1996.
Voraussetzungen / BesonderesVoraussetzung: Lineare Algebra
252-0538-00LShape Modeling and Geometry Processing Information W5 KP2V + 1U + 1AS. Coros
KurzbeschreibungThis course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on polygonal meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry, interactive shape editing, topics in digital shape fabrication.
LernzielThe students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and digital geometry processing.
InhaltRecent advances in 3D digital geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers some of the latest developments in geometric modeling and digital geometry processing. Topics include surface modeling based on triangle meshes, mesh generation, surface reconstruction, mesh fairing and simplification, discrete differential geometry, interactive shape editing and digital shape fabrication.
SkriptSlides and course notes
Voraussetzungen / BesonderesPrerequisites:
Visual Computing, Computer Graphics or an equivalent class. Experience with C++ programming. Some background in geometry or computational geometry is helpful, but not necessary.
252-3900-00LBig Data for Engineers Information
This course is not intended for Computer Science and Data Science students!
W6 KP2V + 2U + 1AG. Fourny
KurzbeschreibungThe key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations.
LernzielThis combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm".

Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small.

The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof.

After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently.
InhaltThis course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe.

It targets specifically students with a scientific or Engineering, but not Computer Science, background.

The material is organized along three axes: data in the large, data in the small, data in the very small. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem.
- physical storage (HDFS, S3)
- logical storage (key-value stores, document stores, column stores, key-value stores, data warehouses)
- data formats and syntaxes (XML, JSON, CSV)
- data shapes and models (tables, trees, graphs)
- an overview of programming languages with a focus on their type systems (SQL, XQuery)
- the most important query paradigms (selection, projection, joining, grouping, ordering, windowing)
- paradigms for parallel processing (MapReduce) and technologies (Hadoop, Spark)
- optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing)
- applications.

Large scale analytics and machine learning are outside of the scope of this course.
LiteraturPapers from scientific conferences and journals. References will be given as part of the course material during the semester.
Voraussetzungen / BesonderesThis course is not intended for Computer Science and Data Science students. Computer Science and Data Science students interested in Big Data MUST attend the Master's level Big Data lecture, offered in Fall.

Requirements: programming knowledge (Java, C++, Python, PHP, ...) as well as basic knowledge on databases (SQL). If you have already built your own website with a backend SQL database, this is perfect.
252-0312-00LUbiquitous Computing Information W3 KP2VF. Mattern, S. Mayer
KurzbeschreibungUbiquitous computing integrates tiny wirelessly connected computers and sensors into the environment and everyday objects. Main topics: The vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact.
LernzielThe vision of ubiquitous computing, trends in technology, smart cards, RFID, Personal Area Networks (Bluetooth), sensor networks, location awareness, privacy and security, application areas, economic and social impact.
SkriptCopies of slides will be made available
LiteraturWill be provided in the lecture. To put you in the mood:
Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
401-4504-18LReading Course: Retarded Potentials and Time Domain Boundary Integral EquationsW4 KP2GR. Hiptmair
KurzbeschreibungBy studying a current monograph and review articles, this course offers a concise introduction into the mathematical theories and techniques underlying time-domain boundary integral equations with focus on wave equations.
LernzielAcquire knowledg about the modern mathematical theory of time-domain boundary integral equations for wave equations.
InhaltRetarded layer potentials.
Distributions and Laplace transform
Convolution quadrature
Time domains analysis of the single layer potential
LiteraturF. Sayas, Retarded potentials and time domain boundary integral equations.
Link
M. Haskell and F.-J. Sayas, Convolution quadrature for wave simulation, Link
Voraussetzungen / BesonderesGeneral knowledge aboput partial differential equations and functional analysis
401-3903-11LGeometric Integer ProgrammingW6 KP2V + 1UR. Weismantel
KurzbeschreibungInteger programming is the task of minimizing a linear function over all the integer points in a polyhedron. This lecture introduces the key concepts of an algorithmic theory for solving such problems.
LernzielThe purpose of the lecture is to provide a geometric treatment of the theory of integer optimization.
InhaltKey topics are:
- lattice theory and the polynomial time solvability of integer optimization problems in fixed dimension,
- the theory of integral generating sets and its connection to totally dual integral systems,
- finite cutting plane algorithms based on lattices and integral generating sets.
Skriptnot available, blackboard presentation
LiteraturBertsimas, Weismantel: Optimization over Integers, Dynamic Ideas 2005.
Schrijver: Theory of linear and integer programming, Wiley, 1986.
Voraussetzungen / Besonderes"Mathematical Optimization" (401-3901-00L)
401-4904-00LCombinatorial Optimization Information W6 KP2V + 1UR. Zenklusen
KurzbeschreibungCombinatorial Optimization deals with efficiently finding a provably strong solution among a finite set of options. This course discusses key combinatorial structures and techniques to design efficient algorithms for combinatorial optimization problems. We put a strong emphasis on polyhedral methods, which proved to be a powerful and unifying tool throughout combinatorial optimization.
LernzielThe goal of this lecture is to get a thorough understanding of various modern combinatorial optimization techniques with an emphasis on polyhedral approaches. Students will learn a general toolbox to tackle a wide range of combinatorial optimization problems.
InhaltKey topics include:
- Polyhedral descriptions;
- Combinatorial uncrossing;
- Ellipsoid method;
- Equivalence between separation and optimization;
- Design of efficient approximation algorithms for hard problems.
SkriptLecture notes will be available online.
Literatur- Bernhard Korte, Jens Vygen: Combinatorial Optimization. 5th edition, Springer, 2012.
- Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency, Springer, 2003. This work has 3 volumes.
Voraussetzungen / BesonderesPrior exposure to Linear Programming can greatly help the understanding of the material. We therefore recommend that students interested in Combinatorial Optimization get familiarized with Linear Programming before taking this lecture.
402-0778-00LParticle Accelerator Physics and Modeling IIW6 KP2V + 1UA. Adelmann
KurzbeschreibungThe effect of nonlinearities on the beam dynamics of charged particles will be discussed. For the nonlinear beam transport, Lie-Methods in combination with differential algebra (DA) and truncated power series (TPS) will be introduced. In the second part we will discuss advanced concepts such as laser plasma wakefield acceleration.
LernzielModel for nonlinear beam dynamics can be applied to new or existing particle accelerators. Some of the most important papers in the field are discussed (as part of the exercises).

Advanced accelerator concepts are analysed and a toy model of a
laser plasma wakefield accelerator is developed.
Inhalt- Symplectic Maps and Higher Order Beam Dynamics
- Taylor Modells and Differential Algebra
- Lie Methods
- Normal Forms
- Coulomb Repulsion (Space Charge) as N-Body Problem
- Coherent Synchrotron Radiation
- Particle Collisions
- Laser Plasma Wakefield Acceleration
SkriptLecture notes
Literatur* Beam Dynamics - A New Attitude and Framework
E. Forest

* Modern Map Methods in Particle Beam Physics
M. Berz (Link)
Voraussetzungen / BesonderesIdeally Particle Accelerator Physics and Modelling 1 (PAM-1), however at the beginning of the semester, a crash course is offered introducing the minimum level of particle accelerator modeling needed to follow. This lecture is also suited for PhD. Students.
402-0738-00LStatistical Methods and Analysis Techniques in Experimental PhysicsW10 KP5GM. Donegà, C. Grab
KurzbeschreibungThis lecture gives an introduction to the statistical methods and the various analysis techniques applied in experimental particle physics. The exercises treat problems of general statistical topics; they also include hands-on analysis projects, where students perform independent analyses on their computer, based on real data from actual particle physics experiments.
LernzielStudents will learn the most important statistical methods used in experimental particle physics. They will acquire the necessary skills to analyse large data records in a statistically correct manner. Learning how to present scientific results in a professional manner and how to discuss them.
InhaltTopics include:
- modern methods of statistical data analysis
- probability distributions, error analysis, simulation methos, hypothesis testing, confidence intervals, setting limits and introduction to multivariate methods.
- most examples are taken from particle physics.

Methodology:
- lectures about the statistical topics;
- common discussions of examples;
- exercises: specific exercises to practise the topics of the lectures;
- all students perform statistical calculations on (their) computers;
- students complete a full data analysis in teams (of two) over the second half of the course, using real data taken from particle physics experiments;
- at the end of the course, the students present their analysis results in a scientific presentation;
- all students are directly tutored by assistants in the classroom.
Skript- Copies of all lectures are available on the web-site of the course.
- A scriptum of the lectures is also available to all students of the course.
Literatur1) Statistics: A guide to the use of statistical medhods in the Physical Sciences, R.J.Barlow; Wiley Verlag .
2) J Statistical data analysis, G. Cowan, Oxford University Press; ISBN: 0198501552.
3) Statistische und numerische Methoden der Datenanalyse, V.Blobel und E.Lohrmann, Teubner Studienbuecher Verlag.
4) Data Analysis, a Bayesian Tutorial, D.S.Sivia with J.Skilling,
Oxford Science Publications.
Voraussetzungen / BesonderesBasic knowlege of nuclear and particle physics are prerequisites.
227-1032-00LNeuromorphic Engineering II Information
Information für UZH Studierende:
Die Lerneinheit kann nur an der ETH belegt werden. Die Belegung des Moduls INI405 ist an der UZH nicht möglich.

Beachten Sie die Einschreibungstermine an der ETH für UZH Studierende: Link
W6 KP5GT. Delbrück, G. Indiveri, S.‑C. Liu
KurzbeschreibungThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".
LernzielDesign of a neuromorphic circuit for implementation with CMOS technology.
InhaltThis course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I".

The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
LiteraturS.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Voraussetzungen / BesonderesPrerequisites: Neuromorphic Engineering I strongly recommended
227-1034-00LComputational Vision (University of Zurich) Information
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI402

Mind the enrolment deadlines at UZH:
Link
W6 KP2V + 1UD. Kiper, K. A. Martin
KurzbeschreibungThis course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
LernzielThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
InhaltThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
LiteraturBooks: (recommended references, not required)
1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997.
2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.
227-1046-00LComputer Simulations of Sensory Systems Information W3 KP2V + 1UT. Haslwanter
KurzbeschreibungThis course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python (or Matlab). The simulations will be such that their output could be used as input for actual neuro-sensory prostheses.
LernzielOur sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system.
The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses.
In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required.
InhaltThe following topics will be covered:
• Introduction into the signal processing in nerve cells.
• Introduction into Python.
• Simplified simulation of nerve cells (Hodgkins-Huxley model).
• Description of the auditory system, including the application of Fourier transforms on recorded sounds.
• Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing.
• Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems).
SkriptFor each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under Link
LiteraturOpen source information is available as wikibook Link

For good overviews I recommend:
• L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702].
This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems.

• Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth
ISBN 0071390111 / 9780071390118
The standard textbook on neuroscience.

• P Wallisch, M Lusignan, M. Benayoun, T. I. Baker, A. S. Dickey, and N. G. Hatsopoulos. MATLAB for Neuroscientists, Academic Press, 2009.
Compactly written, it provides a short introduction to MATLAB, as well as a very good overview of MATLAB’s functionality, focusing on applications in different areas of neuroscience.

• G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)]
A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception.
Voraussetzungen / BesonderesSince I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week).
636-0006-00LComputational Systems Biology: Deterministic Approaches Belegung eingeschränkt - Details anzeigen W4 KP3GJ. Stelling, D. Iber
KurzbeschreibungThe course introduces computat. methods for systems biology under ‘real-world’ conditions of limiting biological knowledge, uncertain model scopes and predictions, and spatial effects. Focus is on systems identification for mechanistic, deterministic models and the corresponding numerical approaches. Topics include uncertainty evaluation, experim. design, and numerical methods for spatial models
LernzielThe aim of the course is to provide students with mathematical and computational methods for the analysis of biological systems in a ‘real world’ setting. This implies (i) incomplete knowledge of components, interactions, and their quantitative features in cellular networks, (ii) resulting uncertainties in model predictions and iterations between models and experiments, and (iii) spatial effects. All these factors make direct representations of biological mechanisms in mechanistic, deterministic mathematical models challenging. Based on general concepts of systems identification and on corresponding numerical methods, the course aims at providing an in-depth understanding of computational approaches that enable the analysis of mechanisms of biological network operation in detail, using iterations between experimental and theoretical systems analysis.
InhaltLecture topics: (1) Mechanistic mathematical models and systems identification challenges; (2-4) Structural models and data integration; (5-8) Identification and experimental design for ODE models; (9-10) Uncertainty quantification; (11-13) Numerical methods for partial differential equation (PDE) models to describe spatial effects.
SkriptCourse material will be made available at: Link
LiteraturBackground literature will be available on-line at the start of the course.
Voraussetzungen / BesonderesFor this advanced course, participants are expected to have a solid background in the mathematical modelling of biological systems, as conveyed by the combination of the following two courses in the MSc Computational Biology and Bioinformatics: ‘Computational systems biology’ and ‘Spatio-temporal modeling in biology’.
636-0016-00LComputational Systems Biology: Stochastic Approaches Information W4 KP3GM. H. Khammash, A. Gupta
KurzbeschreibungThis course is concerned with the development of computational methods for modeling, simulation, and analysis of stochasticity in living cells. Using these tools, the course explores the richness of stochastic phenomena, how it arises from the interactions of dynamics and noise, and its biological implications.
LernzielTo understand the origins and implications of stochastic noise in living cells, and to learn the computational tools for the modeling, simulation, analysis, and identification of stochastic biochemical reaction networks.
InhaltThe cellular environment is abuzz with noise. A key source of this noise is the randomness that characterizes the motion of cellular constituents at the molecular level. Cellular noise not only results in random fluctuations (over time) within individual cells, but it is also a main source of phenotypic variability among clonal cell populations.

Review of basic probability and stochastic processes; Introduction to stochastic gene expression; deterministic vs. stochastic models; the stochastic chemical kinetics framework; a rigorous derivation of the chemical master equation; moment computations; linear vs. nonlinear propensities; linear noise approximations; Monte Carlo simulations; Gillespie's Stochastic Simulation Algorithm (SSA) and variants; direct methods for the solution of the Chemical Master Equation; moment closure methods; intrinsic and extrinsic noise in gene expression; parameter identification from noise; propagation of noise in cell networks; noise suppression in cells; the role of feedback; exploiting noise; bimodality and stochastic switches.
LiteraturLiterature will be distributed during the course as needed.
Voraussetzungen / BesonderesStudents are expected to have completed the course `Mathematical modeling for systems biology (BSc Biotechnology) or `Computational systems biology (MSc Computational biology and bioinformatics). Concurrent enrollment in `Computational Systems Biology: Deterministic Approaches is recommended.
701-0412-00LKlimasystemeW3 KP2GR. Knutti, I. Medhaug
KurzbeschreibungDie wichtigsten physikalischen Komponenten des Klimasystems und deren Wechselwirkungen werden eingeführt. Vor dem Hintergrund der Klimageschichte - und variabilität werden die Mechanismen des anthropogenen Klimawandels analysiert. Absolvierende des Kurses sind in der Lage, einfache Problemstellungen aus dem Bereich der Klimasysteme zu identifizieren und erläutern.
LernzielStudierende können:
- die wichtigsten physikalischen Komponenten des goblaben Klimasystems beschreiben und ihre Wechselwirkungen skizzieren.
- die Mechanismen des anthropogenen Klimawandels erklären.
einfache Problemstellungen aus dem Bereich der Klimasysteme identifizieren und erläutern.
SkriptKopien der Folien werden elektronisch zur Verfuegung gestellt.
LiteraturEine vollständige Literaturliste wird abgegeben. Insbesondere empfohlen sind:
- Hartmann, D., 2016: Global Physical Climatology. Academic Press, London, 485 pp.
- Peixoto, J.P. and A.H. Oort, 1992: Physics of Climate. American Institute of Physics, New York, 520 pp.
Voraussetzungen / BesonderesDozierende: Reto Knutti, mehrere Vorträge zu Spezialthemen von anderen Dozenten
Unterrichtssprache: deutsch
Sprache der Folien: englisch
327-2201-00LTransport Phenomena II Information W5 KP4GH. C. Öttinger
KurzbeschreibungNumerical methods for real-world "Transport Phenomena"; atomistic understanding of transport properties based on kinetic theory and mesoscopic models; fundamentals, applications, and simulations
LernzielThe teaching goals of this course are on five different levels:
(1) Deep understanding of fundamentals: kinetic theory, mesoscopic models, ...
(2) Ability to use the fundamental concepts in applications
(3) Insight into the role of boundary conditions
(4) Knowledge of a number of applications
(5) Flavor of numerical techniques: finite elements, lattice Boltzmann, ...
InhaltThermodynamics of Interfaces
Interfacial Balance Equations
Interfacial Force-Flux Relations
Polymer Processing
Transport Around a Sphere
Refreshing Topics in Equilibrium Statistical Mechanics
Kinetic Theory of Gases
Kinetic Theory of Polymeric Liquids
Transport in Biological Systems
Dynamic Light Scattering
SkriptA detailed manuscript is available; this manuscript will be developed into a book entitled "A Modern Course in Transport Phenomena" by David C. Venerus and Hans Christian Öttinger
Literatur1. R. B. Bird, W. E. Stewart, and E. N. Lightfoot, Transport Phenomena, 2nd Ed. (Wiley, 2001)
2. S. R. de Groot and P. Mazur, Non-Equilibrium Thermodynamics, 2nd Ed. (Dover, 1984)
3. R. B. Bird, Five Decades of Transport Phenomena (Review Article), AIChE J. 50 (2004) 273-287
4. R. Phillips, J. Kondev, and J. Theriot, Physical Biology of the Cell (Garland, 2008)
5. G. A. Truskey, F. Yuan, and D. F. Katz, Transport Phenomena in Biological Systems (Prentice Hall, 2004)
Voraussetzungen / BesonderesComplex numbers. Vector analysis (integrability; Gauss' divergence theorem). Laplace and Fourier transforms. Ordinary differential equations (basic ideas). Linear algebra (matrices; functions of matrices; eigenvectors and eigenvalues; eigenfunctions). Probability theory (Gaussian distributions; Poisson distributions; averages; moments; variances; random variables). Numerical mathematics (integration). Statistical thermodynamics (Gibbs' fundamental equation; thermodynamic potentials; Legendre transforms; Gibbs' phase rule; ergodicity; partition functions; Einstein's fluctuation theory). Linear irreversible thermodynamics (forces and fluxes; Fourier's, Newton's and Fick's laws for fluxes). Hydrodynamics (local equilibrium; balance equations for mass, momentum, energy and entropy). Programming and simulation techniques (Matlab, Monte Carlo simulations).
» siehe auch Angebot im Abschnitt Vertiefungsgebiete
Fallstudien
NummerTitelTypECTSUmfangDozierende
401-3667-18LCase Studies Seminar (Spring Semester 2018) Information W3 KP2SV. C. Gradinaru, R. Hiptmair, K. Nipp, M. Reiher
KurzbeschreibungIn the CSE Case Studies Seminar invited speakers from ETH, from other universities as well as from industry give a talk on an applied topic. Beside of attending the scientific talks students are asked to give short presentations (10 minutes) on a published paper out of a list.
Lernziel
InhaltIn the CSE Case Studies Seminar invited speakers from ETH, from other universities as well as from industry give a talk on an applied topic. Beside of attending the scientific talks students are asked to give short presentations (10 minutes) on a published paper out of a list (containing articles from, e.g., Nature, Science, Scientific American, etc.).
Semesterarbeit
Es gibt mehrere Lerneinheiten "Semesterarbeit", die alle gleichwertig sind. Wenn Sie im Lauf Ihres Studiums mehrere Semesterarbeiten schreiben, wählen Sie jeweils verschiedene Nummern aus, um wieder Kreditpunkte erhalten zu können.
NummerTitelTypECTSUmfangDozierende
401-3740-01LSemesterarbeit Belegung eingeschränkt - Details anzeigen
Voraussetzung: erfolgreicher Abschluss der Lerneinheit 401-2000-00L Scientific Works in Mathematics oder 402-2000-00L Scientific Works in Physics
Weitere Angaben unter Link
Nur für Semesterarbeiten zugelassene Betreuer müssen durch das Studiensekretariat zugeordnet werden.
W8 KP11ABetreuer/innen
KurzbeschreibungSemesterarbeiten dienen der Vertiefung in einem spezifischen Fachbereich; die Themen werden den Studierenden zur individuellen Auswahl angeboten. Semesterarbeiten sollen die Fähigkeit der Studierenden zu selbständiger mathematischer Tätigkeit und zur schriftlichen Darstellung mathematischer Ergebnisse fördern.
Lernziel
Voraussetzungen / BesonderesEs gibt mehrere Lerneinheiten "Semesterarbeit", die alle gleichwertig sind. Wenn Sie im Lauf Ihres Studiums mehrere Semesterarbeiten schreiben, wählen Sie jeweils verschiedene Nummern aus, um wieder Kreditpunkte erhalten zu können.
401-3740-02LSemesterarbeit Belegung eingeschränkt - Details anzeigen
Voraussetzung: erfolgreicher Abschluss der Lerneinheit 401-2000-00L Scientific Works in Mathematics oder 402-2000-00L Scientific Works in Physics
Weitere Angaben unter Link
Nur für Semesterarbeiten zugelassene Betreuer müssen durch das Studiensekretariat zugeordnet werden.
W8 KP11ABetreuer/innen
KurzbeschreibungSemesterarbeiten dienen der Vertiefung in einem spezifischen Fachbereich; die Themen werden den Studierenden zur individuellen Auswahl angeboten. Semesterarbeiten sollen die Fähigkeit der Studierenden zu selbständiger mathematischer Tätigkeit und zur schriftlichen Darstellung mathematischer Ergebnisse fördern.
Lernziel
Voraussetzungen / BesonderesEs gibt mehrere Lerneinheiten "Semesterarbeit", die alle gleichwertig sind. Wenn Sie im Lauf Ihres Studiums mehrere Semesterarbeiten schreiben, wählen Sie jeweils verschiedene Nummern aus, um wieder Kreditpunkte erhalten zu können.
GESS Wissenschaft im Kontext
» Empfehlungen aus dem Bereich Wissenschaft im Kontext (Typ B) für das D-MATH
» siehe Studiengang Wissenschaft im Kontext: Sprachkurse ETH/UZH
» siehe Studiengang Wissenschaft im Kontext: Typ A: Förderung allgemeiner Reflexionsfähigkeiten
Master-Arbeit
Wenn Sie anstelle von 401-2000-00L Scientific Works in Mathematics die Lerneinheit 402-2000-00L Scientific Works in Physics anrechnen lassen möchten (dies ist erlaubt im Studiengang Rechnergestützte Wissenschaften), so wenden Sie sich nach dem Verfügen des Resultates an das Studiensekretariat (Link).
NummerTitelTypECTSUmfangDozierende
401-2000-00LScientific Works in Mathematics
Zielpublikum:
Bachelor-Studierende im dritten Jahr;
Master-Studierende, welche noch keine entsprechende Ausbildung vorweisen können.
O0 KPE. Kowalski
KurzbeschreibungIntroduction to scientific writing for students with focus on publication standards and ethical issues, especially in the case of citations (references to works of others.)
LernzielLearn the basic standards of scientific works in mathematics.
Inhalt- Types of mathematical works
- Publication standards in pure and applied mathematics
- Data handling
- Ethical issues
- Citation guidelines
SkriptMoodle of the Mathematics Library: Link
Voraussetzungen / BesonderesThis course is completed by the optional course "Recherchieren in der Mathematik" (held in German) by the Mathematics Library. For more details see: Link

Weisung Link
401-2000-01LRecherchieren in der Mathematik
Für Details und zur Registrierung für den freiwilligen MathBib-Schulungskurs: Link
Z0 KPReferent/innen
KurzbeschreibungFreiwilliger Kurs "Recherchieren in der Mathematik" angeboten von der Mathematikbibliothek.
Lernziel
402-2000-00LScientific Works in Physics
Zielpublikum:
Master-Studierende, welche noch keine entsprechende Ausbildung vorweisen können.

Weisung Link
W0 KPC. Grab
KurzbeschreibungLiterature Review: ETH-Library, Journals in Physics, Google Scholar; Thesis Structure: The IMRAD Model; Document Processing: LaTeX and BibTeX, Mathematical Writing, AVETH Survival Guide; ETH Guidelines for Integrity; Authorship Guidelines; ETH Citation Etiquettes; Declaration of Originality.
LernzielBasic standards for scientific works in physics: How to write a Master Thesis. What to know about research integrity.
401-4990-01LMaster's Thesis Belegung eingeschränkt - Details anzeigen
Zur Master-Arbeit wird nur zugelassen, wer:
a. das Bachelor-Studium erfolgreich abgeschlossen hat;
b. allfällige Auflagen für die Zulassung zum Master-Studiengang erfüllt hat; und
c. im Master-Studium mindestens die folgenden Studienleistungen erbracht hat:
1) in der Kategorie "Kernfächer" müssen mindestens zwei Lerneinheiten bestanden sein;
2) in der Kategorie "Vertiefungsgebiete" müssen mindestens fünf Lerneinheiten, davon ein Seminar, bestanden sein; und
3) die Semesterarbeit muss bestanden sein.

Voraussetzung: erfolgreicher Abschluss der Lerneinheit 401-2000-00L Scientific Works in Mathematics oder 402-2000-00L Scientific Works in Physics
Weitere Angaben unter Link
O30 KP57DBetreuer/innen
KurzbeschreibungDie Master-Arbeit bildet den Abschluss des Studiengangs. Die Studierenden sollen mit der Master-Arbeit ihre Fähigkeit zu selbständiger, strukturierter und wissenschaftlicher Tätigkeit unter Beweis stellen.
LernzielDie Studierenden sollen mit der Master-Arbeit, die den Abschluss des Studiengangs bildet, ihre Fähigkeit zu selbständiger, strukturierter und wissenschaftlicher Tätigkeit unter Beweis stellen.
Kolloquien
NummerTitelTypECTSUmfangDozierende
401-5650-00LZurich Colloquium in Applied and Computational Mathematics Information E-0 KP1KR. Abgrall, R. Alaifari, H. Ammari, R. Hiptmair, A. Jentzen, S. Mishra, S. Sauter, C. Schwab
KurzbeschreibungForschungskolloquium
Lernziel
Auflagen-Lerneinheiten
Das untenstehende Lehrangebot gilt nur für MSc Studierende mit Zulassungsauflagen.
NummerTitelTypECTSUmfangDozierende
151-0102-AALFluid Dynamics I
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-6 KP13RT. Rösgen
KurzbeschreibungAn introduction to the physical and mathematical foundations of fluid dynamics is given.
Topics include dimensional analysis, integral and differential conservation laws, inviscid and viscous flows, Navier-Stokes equations, boundary layers, turbulent pipe flow. Elementary solutions and examples are presented.
LernzielAn introduction to the physical and mathematical principles of fluid dynamics. Fundamental terminology/principles and their application to simple problems.
InhaltPhänomene, Anwendungen, Grundfragen
Dimensionsanalyse und Ähnlichkeit; Kinematische Beschreibung; Erhaltungssätze (Masse, Impuls, Energie), integrale und differentielle Formulierungen; Reibungsfreie Strömungen: Euler-Gleichungen, Stromfadentheorie, Satz von Bernoulli; Reibungsbehaftete Strömungen: Navier-Stokes-Gleichungen; Grenzschichten; Turbulenz
SkriptEine erweiterte Formelsammlung zur Vorlesung wird elektronisch zur Verfügung gestellt.
LiteraturEmpfohlenes Buch: Fluid Mechanics, P. Kundu & I. Cohen, Elsevier
Voraussetzungen / BesonderesPerformance Assessment: session examination
Allowed aids:
Textbook (free selection, list of assignments), list of formulars IFD, 8 Sheets (=4 Pages) own notes, calculator
252-0232-AALSoftware Design Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-6 KP13RD. Gruntz
KurzbeschreibungIm Kurs Software Design werden häufig verwendete Entwurfsmuster der objektorientierten Programmierung und des objektorientierten Designs vorgestellt und diskutiert. Die behandelten Muster werden mit Beispielen aus den Java Bibliotheken illustriert und in einem Projekt angewendet.
LernzielDie Studierenden
- kennen die Grundprinzipien der objektorientierten Programmierung und können diese anwenden.
- kennen die wichtigsten objektorientierten Entwurfsmuster.
- können diese anwenden um Designprobleme zu lösen.
- erkennen in einem gegebenen Design die Verwendung von Entwurfsmustern.
InhaltIn der Vorlesung wird in die objektorientierte Programmierung eingeführt. Als Programmiersprache wird Java verwendet. Der Fokus liegt jedoch auf dem objektorientierten Design, d.h. auf Entwurfsmustern. Entwurfsmuster sind Lösungen für wiederkehrende Designprobleme. Die behandelten Muster werden mit Beispielen aus den Java Bibliotheken illustriert und in einem Projekt angewendet.
Literatur- Gamma, Helm, Johnson, Vlissides; Entwurfsmuster als Elemente wiederverwendbarer objektorientierter Systeme; mitp 2014; ISBN 978-3826697005
- Freeman, Freeman, Sierra; Entwurfsmuster von Kopf bis Fuss, Head First Design Patterns, Head First Design Patterns; O‘Reilly; ISBN 978-3955619862
406-0353-AALAnalysis III
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RF. Da Lio
KurzbeschreibungThe focus lies on the simplest cases of three fundamental types of partial differential equations of second order: the Laplace equation, the heat equation and the wave equation.
Lernziel
LiteraturReference books and notes

Main books:

Giovanni Felder: "Partielle Differenzialgleichungen für Ingenieurinnen und Ingenieure" (Download PDF: Link ),
Erwin Kreyszig: "Advanced Engineering Mathematics", John Wiley & Sons, just chapters 11, 16.


Extra readings:

Norbert Hungerbühler: "Einführung in die partiellen Differentialgleichungen", vdf Hochschulverlag AG an der ETH Zürich,
Yehuda Pinchover, Jacob Rubinstein: "Partial Differential Equations", Cambridge University Press 2005.


For reference/complement of the Analysis I/II courses:

Christian Blatter: Ingenieur-Analysis (Download PDF)
Voraussetzungen / BesonderesThe precise content changes with the examiner. Candidates must therefore contact the examiner in person before studying the material.
406-0603-AALStochastics (Probability and Statistics)
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RM. Kalisch
KurzbeschreibungIntroduction to basic methods and fundamental concepts of statistics and
probability theory for non-mathematicians. The concepts are presented on
the basis of some descriptive examples. The course will be based on the
book "Statistics for research" by S. Dowdy et.al. and on the
book "Introductory Statistics with R" by P. Dalgaard.
LernzielThe objective of this course is to build a solid fundament in probability
and statistics. The student should understand some fundamental concepts and
be able to apply these concepts to applications in the real
world. Furthermore, the student should have a basic knowledge of the
statistical programming language "R". The main topics of the course are:
- Introduction to probability
- Common distributions
- Binomialtest
- z-Test, t-Test
- Regression
InhaltFrom "Statistics for research":
Ch 1: The Role of Statistics
Ch 2: Populations, Samples, and Probability Distributions
Ch 3: Binomial Distributions
Ch 6: Sampling Distribution of Averages
Ch 7: Normal Distributions
Ch 8: Student's t Distribution
Ch 9: Distributions of Two Variables [Regression]

From "Introductory Statistics with R":
Ch 1: Basics
Ch 2: Probability and distributions
Ch 3: Descriptive statistics and tables
Ch 4: One- and two-sample tests
Ch 5: Regression and correlation
Literatur"Statistics for research" by S. Dowdy et. al. (3rd
edition); Print ISBN: 9780471267355; Online ISBN: 9780471477433; DOI:
10.1002/0471477435;
From within the ETH, this book is freely available online under:
Link

"Introductory Statistics with R" by Peter Dalgaard; ISBN
978-0-387-79053-4; DOI: 10.1007/978-0-387-79054-1
From within the ETH, this book is freely available online under:
Link
406-0663-AALNumerical Methods for CSE Information
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-7 KP15RR. Alaifari
KurzbeschreibungIntroduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology.
Lernziel* Knowledge of the fundamental algorithms in numerical mathematics
* Knowledge of the essential terms in numerical mathematics and the
techniques used for the analysis of numerical algorithms
* Ability to choose the appropriate numerical method for concrete problems
* Ability to interpret numerical results
* Ability to implement numerical algorithms afficiently in C++
Inhalt1. Computing with Matrices and Vectors
2. Direct Methods for Linear Systems of Equations
3. Direct Methods for Linear Least Squares Problems
4. Filtering Algorithms
5. Data Interpolation and Data Fitting in 1D
6. Approximation of Functions in 1D
7. Numerical Quadrature
8. Iterative Methods for Non-linear Systems of Equations
12. Numerical Integration - Single Step Methods
13. Single Step Methods for Stiff Initial Value Problems
SkriptLink
LiteraturW. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006.
M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002
P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002
U. Ascher and C. Greif "A first course in Numerical Methods"
Voraussetzungen / BesonderesExamination will be conducted at the computer and will involve coding in C++/Eigen.
A course covering the material is taught in English every autumn term (course unit 401-0663-00L). Course documents, exercises and examinations are available online.
529-0483-AALStatistical Physics and Computer Simulation
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle anderen Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
E-4 KP9RM. Reiher
KurzbeschreibungDie statistische Mechanik verbindet die detaillierte Beschreibung der mikroskopischen Viel-Teilchen-Dynamik mit der phänomenologischen, gemittelten Beschreibung des makroskopischen Benehmens eines Systems. Sie wird mittels Computersimulationen dargelegt. Prinzipien und Anwendungen der statistischen Mechanik und Gleichgewichts-Molekulardynamik; Monte-Carlo-Verfahren.
LernzielEinführung in die statistische Mechanik mit Hilfe von Computersimulationen, erwerben der Fertigkeit Computersimulationen durchzuführen und die Resultate zu interpretieren.
InhaltDie statistische Mechanik verbindet die detaillierte Beschreibung der mikroskopischen Viel-Teilchen-Dynamik mit der phänomenologischen, gemittelten Beschreibung des makroskopischen Benehmens eines Systems. Die statistisceh Mechanik wird mit Hilfe von Computersimulationen dargelegt.
Prinzipien und Anwendungen der statistischen Mechanik und Gleichgewichts-Molekulardynamik; Monte-Carlo-Verfahren; Prinzipien und Anwendungen der stochastischen Dynamik; Einführung und Anwendungne der Nichtgleichgewichts-Molekulardynamik.
Literatursiehe "Course Schedule"