Search result: Catalogue data in Spring Semester 2021

Computational Science and Engineering Bachelor Information
Bachelor Studies (Programme Regulations 2016)
Basic Courses
Block G4
Students that enrol for the second year in the CSE Bachelor Programme and whose first year examination did not involve the subject "Physics I" will instead of "Physics II" (402-0034-10L) take the "Physics I and II" (402-0043-00L and 402-0044-00L) courses with performance assessment as a yearly course.
As of FS 2018 the course unit 151-0122-00L Fluid Dynamics for CSE gets replaced in Block G4 by 151-0102-00L Fluid Dynamics I.
402-0034-10LPhysics IIW4 credits2V + 2UW. Wegscheider
AbstractThis is a two-semester course introducing students into the foundations of Modern Physics. Topics include electricity and magnetism, light, waves, quantum physics, solid state physics, and semiconductors. Selected topics with important applications in industry will also be considered.
ObjectiveThe lecture is intended to promote critical, scientific thinking. Key concepts of Physics will be acquired, with a focus on technically relevant applications. At the end of the two semesters, students will have a good overview over the topics of classical and modern Physics.
ContentIntroduction into Quantum Physics, Absorption and Emission of Electromagnetic Radiation, Basics of Solid State Physics, Semiconductors
Lecture notesLecture notes will be available in German.
LiteraturePaul A. Tipler, Gene Mosca, Michael Basler und Renate Dohmen
Physik: für Wissenschaftler und Ingenieure
Spektrum Akademischer Verlag, 2009, 1636 Seiten, ca. 80 Euro.

Paul A. Tipler, Ralph A. Llewellyn
Moderne Physik
Oldenbourg Wissenschaftsverlag, 2009, 982 Seiten, ca. 75 Euro.
Prerequisites / NoticeNo testat requirements for this lecture.
402-0044-00LPhysics IIW4 credits3V + 1UT. Esslinger
AbstractIntroduction to the concepts and tools in physics with the help of demonstration experiments: electromagnetism, optics, introduction to modern physics.
ObjectiveThe concepts and tools in physics, as well as the methods of an experimental science are taught. The student should learn to identify, communicate and solve physical problems in his/her own field of science.
ContentElectromagnetism (electric current, magnetic fields, electromagnetic induction, magnetic materials, Maxwell's equations)
Optics (light, geometrical optics, interference and diffraction)
Short introduction to quantum physics
Lecture notesThe lecture follows the book "Physik" by Paul A. Tipler.
LiteraturePaul A. Tipler and Gene Mosca
Springer Spektrum Verlag
151-0102-00LFluid Dynamics I Restricted registration - show details O6 credits4V + 2UT. Rösgen
AbstractAn 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.
ObjectiveAn introduction to the physical and mathematical principles of fluid dynamics. Fundamental terminology/principles and their application to simple problems.
ContentPhenomena, applications, foundations
dimensional analysis and similitude; kinematic description; conservation laws (mass, momentum, energy), integral and differential formulation; inviscid flows: Euler equations, stream filament theory, Bernoulli equation; viscous flows: Navier-Stokes equations; boundary layers; turbulence
Lecture notesLecture notes (extended formulary) for the course are made available electronically.
LiteratureRecommended book: Fluid Mechanics, Kundu & Cohen & Dowling, 6th ed., Academic Press / Elsevier (2015).
Prerequisites / NoticeVoraussetzungen: Physik, Analysis
529-0483-00LStatistical Physics and Computer Simulation Information O4 credits2V + 1US. Riniker, P. H. Hünenberger
AbstractPrinciples and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, Stochastic dynamics.
Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages.
ObjectiveIntroduction to statistical mechanics with the aid of computer simulation, development of skills to carry out statistical mechanical calculations using computers and interpret the results.
ContentPrinciples and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, stochastic dynamics, free energy calculation.
Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages.
Literaturewill be announced in the lecture
Prerequisites / NoticeSince the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the written exam, the results of a small programming project will be presented in a 10-minutes talk by pairs of students who had been working on the project.

Additional information will be provided in the first lecture.
Core Courses
151-0116-00LHigh Performance Computing for Science and Engineering (HPCSE) for CSE Information O7 credits4G + 2PP. Koumoutsakos, S. M. Martin
AbstractThis course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Bayesian Uncertainty Quantification and Machine Learning including the implementation of these algorithms on HPC architectures.
ObjectiveThe course will teach
- programming models and tools for multi and many-core architectures
- fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences.
- fundamentals of Deep Learning
ContentHigh Performance Computing:
- Advanced topics in shared-memory programming
- Advanced topics in MPI
- GPU architectures and CUDA programming

Uncertainty Quantification:
- Uncertainty quantification under parametric and non-parametric modeling uncertainty
- Bayesian inference with model class assessment
- Markov Chain Monte Carlo simulation

Machine Learning
- Deep Neural Networks and Stochastic Gradient Descent
- Deep Neural Networks for Data Compression (Autoencoders)
- Recurrent Neural Networks
Lecture notesLink
Class notes, handouts
Literature- Class notes
- Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein
- CUDA by example, J. Sanders and E. Kandrot
- Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling
- An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas
- Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin
- Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides
Prerequisites / NoticeAttendance of HPCSE I
252-0232-00LSoftware Engineering Information O6 credits2V + 1UF. Friedrich Wicker, M. Schwerhoff
AbstractThis course introduces both theoretical and applied aspects of software engineering. It covers:

- Software Architecture
- Informal and formal Modeling
- Design Patterns
- Software Engineering Principles
- Code Refactoring
- Program Testing
ObjectiveThe course has two main objectives:

- Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software.
- Be able to apply these techniques in practice.
ContentWhile the lecture will provide the theoretical foundations for the various aspects of software engineering, the students will apply those techniques in project work that will span over the whole semester - involving all aspects of software engineering, from understanding requirements over design and implementation to deployment and change requests.
Lecture notesno lecture notes
LiteratureWill be announced in the lecture
Bachelor's Thesis
If you wish to have recognised 402-2000-00L Scientific Works in Physics instead of 401-2000-00L Scientific Works in Mathematics (as allowed for the CSE programme), take contact with the Study Administration Office (Link) after having passed the performance assessment.
401-2000-00LScientific Works in Mathematics
Target audience:
Third year Bachelor students;
Master students who cannot document to have received an adequate training in working scientifically.
O0 creditsM. Burger
AbstractIntroduction to scientific writing for students with focus on publication standards and ethical issues, especially in the case of citations (references to works of others.)
ObjectiveLearn the basic standards of scientific works in mathematics.
Content- Types of mathematical works
- Publication standards in pure and applied mathematics
- Data handling
- Ethical issues
- Citation guidelines
Lecture notesMoodle of the Mathematics Library: Link
Prerequisites / NoticeDirective Link
401-2000-01LLunch Sessions – Thesis Basics for Mathematics Students
Details and registration for the optional MathBib training course: Link
Z0 creditsSpeakers
AbstractOptional course "Recherchieren in der Mathematik" (held in German) by the Mathematics Library.
402-2000-00LScientific Works in Physics
Target audience:
Master students who cannot document to have received an adequate training in working scientifically.

Directive Link
W0 creditsC. Eichler
AbstractLiterature 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.
ObjectiveBasic standards for scientific works in physics: How to write a Master Thesis. What to know about research integrity.
401-3990-01LBachelor's Thesis Restricted registration - show details
Only for Computational Science and Engineering BSc, Programme Regulations 2012 and 2016.

Successful participation in the course unit 401-2000-00L Scientific Works in Mathematics or 402-2000-00L Scientific Works in Physicsis is required.
For more information, see Link
O8 credits11DSupervisors
AbstractThe BSc thesis concludes the curriculum. In their BSc thesis, students should demonstrate their ability to carry out independent, structured scientific work. The purpose of the BSc thesis is to deepen knowledge in a certain subject and to bring students into closer contact with applications in an existing computational group. The BSc thesis requires approximately 160 hours of work.
ObjectiveIn their Bsc thesis students should demonstrate their ability to carry out independent, structured scientific work. The purpose is to deepen knowledge in a certain subject and to enable students to collaborate in an existing scientific group to take a computational approach to problems encountered in applications.
Prerequisites / NoticeThe supervisor responsible for the Bachelor thesis defines the task and determines the start and the submission date. The Bachelor thesis concludes with a written report. The Bachelor thesis is graded.
For All Programme Regulations
Fields of Specialization
402-0394-00LTheoretical Cosmology
Special Students UZH must book the module AST513 directly at UZH.
W10 credits4V + 2UL. M. Mayer, J. Yoo
AbstractThis 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.
ObjectiveLearning the fundamentals of modern physical cosmology. This
entails understanding the physical principles behind the description
of the homogeneous Universe on large scales in the first part of the
course, and moving on to the inhomogeneous Universe model where
perturbation theory is used to study the development of structure
through gravitational instability in the second part of the course.
Modern notions of dark matter and dark energy will also be introduced and discussed.
ContentThe 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
Lecture notesIn 2021, the lectures will be live-streamed online at ETH from the Room HPV G5 at the lecture hours. The recordings will be available at the ETH website. The detailed information will be provided by the course website and the SLACK channel.
LiteratureSuggested 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
Prerequisites / NoticeKnowledge of General Relativity is recommended.
Physics of the Atmosphere
701-1216-00LNumerical Modelling of Weather and Climate Information W4 credits3GC. Schär, J. Vergara Temprado, M. Wild
AbstractThe course provides an introduction to weather and climate models. It discusses how these models are built addressing both the dynamical core and the physical parameterizations, and it provides an overview of how these models are used in numerical weather prediction and climate research. As a tutorial, students conduct a term project and build a simple atmospheric model using the language PYTHON.
ObjectiveAt the end of this course, students understand how weather and climate models are formulated from the governing physical principles, and how they are used for climate and weather prediction purposes.
ContentThe 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.
Lecture notesSlides and lecture notes will be made available at
LiteratureList of literature will be provided.
Prerequisites / NoticePrerequisites: 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. Previous experience with PYTHON is useful but not required.
529-0474-00LQuantum ChemistryW6 credits3GM. Reiher, T. Weymuth
AbstractIntroduction into the basic concepts of electronic structure theory and into numerical methods of quantum chemistry. Exercise classes are designed to deepen the theory; practical case studies using quantum chemical software to provide a 'hands-on' expertise in applying these methods.
ObjectiveNowadays, chemical research can be carried out in silico, an intellectual achievement for which Pople and Kohn have been awarded the Nobel prize of the year 1998. This lecture shows how that has been accomplished. It works out the many-particle theory of many-electron systems (atoms and molecules) and discusses its implementation into computer programs. A complete picture of quantum chemistry shall be provided that will allow students to carry out such calculations on molecules (for accompanying experimental work in the wet lab or as a basis for further study of the theory).
ContentBasic concepts of many-particle quantum mechanics. Derivation of the many-electron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with Hartree-Fock theory for the electronic problem to Moeller-Plesset perturbation theory and configuration interaction and to coupled cluster and multi-configurational approaches. Density functional theory. Case studies using quantum mechanical software.
Lecture notesHand-outs in German will be provided for each lecture (they are supplemented by (computer) examples that continuously illustrate how the theory works).

All information regarding this course, including links to the online streaming, will be available on this web page:
LiteratureTextbooks on Quantum Chemistry:
F.L. Pilar, Elementary Quantum Chemistry, Dover Publications
I.N. Levine, Quantum Chemistry, Prentice Hall

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

Textbooks on Computational Chemistry:
F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons
C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons
Prerequisites / NoticeBasic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required
227-0161-00LMolecular and Materials Modelling Information W4 credits2V + 2UD. Passerone, C. Pignedoli
AbstractThe course introduces the basic techniques to interpret experiments with contemporary atomistic simulation, including force fields or ab initio based molecular dynamics and Monte Carlo. Structural and electronic properties will be simulated hands-on for realistic systems.
The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples.
ObjectiveThe 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, 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.
Content-Classical force fields in molecular and condensed phase systems
-Methods for finding stationary states in a potential energy surface
-Monte Carlo techniques applied to nanoscience
-Classical molecular dynamics: extracting quantities and relating to experimentally accessible properties
-From molecular orbital theory to quantum chemistry: chemical reactions
-Condensed phase systems: from periodicity to band structure
-Larger scale systems and their electronic properties: density functional theory and its approximations
-Advanced molecular dynamics: Correlation functions and extracting free energies
-The use of Smooth Overlap of Atomic Positions (SOAP) descriptors in the evaluation of the (dis)similarity of crystalline, disordered and molecular compounds
Lecture notesA script will be made available and complemented by literature references.
LiteratureD. 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.

C. J. Cramer, Essentials of Computational Chemistry. Theories and Models, Wiley 2004

G. L. Miessler, P. J. Fischer, and Donald A. Tarr, Inorganic Chemistry, Pearson 2014.

K. Huang, Statistical Mechanics, Wiley, 1987.

N. W. Ashcroft, N. D. Mermin, Solid State Physics, Saunders College 1976.

E. Kaxiras, Atomic and Electronic Structure of Solids, Cambridge University Press 2010.
Fluid Dynamics
151-0208-00LComputational Methods for Flow, Heat and Mass Transfer ProblemsW4 credits4GD. W. Meyer-Massetti
AbstractNumerical methods for the solution of flow, heat & mass transfer problems are presented and illustrated by analytical & computer exercises.
ObjectiveKnowledge of and practical experience with discretization and solution methods for computational fluid dynamics and heat and mass transfer problems
Content- Introduction with application examples, steps to a numerical solution
- Classification of PDEs, application examples
- Finite differences
- Finite volumes
- Method of weighted residuals, spectral methods, finite elements
- Stability analysis, consistency, convergence
- Numerical solution methods, linear solvers
The learning materials are illustrated with practical examples.
Lecture notesSlides to be completed during the lecture will be handed out.
LiteratureReferences are provided during the lecture. Notes in close agreement with the lecture material are available (in German).
Prerequisites / NoticeBasic knowledge in fluid dynamics, thermodynamics and programming (lecture: "Models, Algorithms and Data: Introduction to Computing")
Systems and Control
227-0216-00LControl Systems II Information W6 credits4GR. Smith
AbstractIntroduction to basic and advanced concepts of modern feedback control.
ObjectiveIntroduction to basic and advanced concepts of modern feedback control.
ContentThis 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.
Lecture notesThe slides of the lecture are available to download.
LiteratureSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / NoticePrerequisites:
Control Systems or equivalent
227-0046-10LSignals and Systems IIW4 credits2V + 2UJ. Lygeros
AbstractContinuous and discrete time linear system theory, state space methods, frequency domain methods, controllability, observability, stability.
ObjectiveIntroduction to basic concepts of system theory.
ContentModeling and classification of dynamical systems.

Modeling of linear, time invariant systems by state equations. Solution of state equations by time domain and Laplace methods. Stability, controllability and observability analysis. Frequency domain description, Bode and Nyquist plots. Sampled data and discrete time systems.

Advanced topics: Nonlinear systems, chaos, discrete event systems, hybrid systems.
Lecture notesCopy of transparencies
K.J. Astrom and R. Murray, "Feedback Systems: An Introduction for Scientists and Engineers", Princeton University Press 2009

151-0854-00LAutonomous Mobile Robots Information W5 credits4GR. Siegwart, M. Chli, N. Lawrance
AbstractThe 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, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
ObjectiveThe 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, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
Lecture notesThis 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.
LiteratureThis 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 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction 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.
Lecture notesLecture notes available on course website: Link
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
252-0579-00L3D Vision Information W5 credits3G + 1AM. Pollefeys, V. Larsson
AbstractThe 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.
ObjectiveAfter 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.
ContentThe 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.
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