Suchergebnis: Katalogdaten im Frühjahrssemester 2021

Rechnergestützte Wissenschaften Bachelor Information
Für alle Studienreglemente
Vertiefungsgebiete
Astrophysik
NummerTitelTypECTSUmfangDozierende
402-0394-00LTheoretical Cosmology
Fachstudierende UZH müssen das Modul AST513 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.
LernzielLearning 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.
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
SkriptIn 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.
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, J. Vergara Temprado, M. Wild
KurzbeschreibungThe 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.
LernzielAt 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.
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. Previous experience with PYTHON is useful but not required.
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 Moleküle) 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 Moleküle; beginnend bei der harmonischen Näherung für das Kern-Problem und bei der Hartree-Fock-Theorie für das elektronische Problem über Moeller-Plesset-Störungstheorie 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).

Sämtliche Informationen zur Vorlesung, sowie die links zum Online-Streaming werden auf dieser Webseite bekanntgegeben:
Link
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)
227-0161-00LMolecular and Materials Modelling Information W4 KP2V + 2UD. Passerone, C. Pignedoli
KurzbeschreibungThe 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.
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, 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.
Inhalt-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
SkriptA script will be made available and complemented by literature references.
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.

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.
Fluiddynamik
NummerTitelTypECTSUmfangDozierende
151-0208-00LComputational Methods for Flow, Heat and Mass Transfer ProblemsW4 KP4GD. W. Meyer-Massetti
KurzbeschreibungEs werden numerische Methoden zur Lösung von Problemen der Fluiddynamik, Energie- & Verfahrenstechnik dargestellt und anhand von analytischen & numerischen Beispielen illustriert.
LernzielKenntnisse und praktische Erfahrung mit der Anwendung von Diskretisierungs- und Lösungsverfahren für Problem der Fluiddynamik und der Energie- und Verfahrenstechnik
Inhalt- Einleitung mit Anwendungen, Schritte zur numerischen Lösung
- Klassifizierung partieller Differentialgleichungen, Beispiele aus Anwendungen
- Finite Differenzen
- Finite Volumen
- Methoden der gewichteten Residuen, Spektralmethoden, finite Elemente
- Stabilitätsanalyse, Konsistenz, Konvergenz
- Numerische Lösungsverfahren, lineare Löser
Der Stoff wird mit Beispielen aus der Praxis illustriert.
SkriptFolien zur Ergänzung während der Vorlesung werden ausgegeben.
LiteraturReferenzen werden in der Vorlesung angegeben. Notizen in guter Übereinstimmung mit der Vorlesung stehen zur Verfügung.
Voraussetzungen / BesonderesGrundlagen in Fluiddynamik, Thermodynamik und Programmieren (Vorlesung: "Models, Algorithms and Data: Introduction to Computing")
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
227-0046-10LSignal- und Systemtheorie IIW4 KP2V + 2UJ. Lygeros
KurzbeschreibungZeitkontinuierliche und zeitdiskrete lineare Systemtheorie, Zustandsraummethoden, Frequenzbereichmethoden, Steuerbarkeit, Beobachtbarkeit, Stabilität.
LernzielEinführung in die Grundkonzepte der Systemtheorie
InhaltModellierung und Typenbezeichnung von dynamischen Systemen.

Modellierung von linearen, zeitinvarianten Systemen durch Zustandsgleichungen. Lösung von Zustandsgleichungen durch Zeitbereich- und Laplacebereichmethoden. Stabilitäts-, Steuerbarkeits- und Beobachtbarkeitsanalyse. Beschreibung im Frequenzbereich, Bode- und Nyquistdiagramm. Abgetastete und zeitdiskrete Systeme.

Weiterführende Themen: Nichtlineare Systeme, Chaos, Diskrete Ereignissysteme, Hybride Systeme.
SkriptKopie der Folien
LiteraturEmpfohlen:
K.J. Astrom and R. Murray, "Feedback Systems: An Introduction for Scientists and Engineers", Princeton University Press 2009

Link
Robotik
NummerTitelTypECTSUmfangDozierende
151-0854-00LAutonomous Mobile Robots Information W5 KP4GR. Siegwart, M. Chli, N. Lawrance
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, 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.
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, environment 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.
252-0579-00L3D Vision Information W5 KP3G + 1AM. Pollefeys, V. Larsson
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.
252-0220-00LIntroduction to Machine Learning Information Belegung eingeschränkt - Details anzeigen
Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact Link
W8 KP4V + 2U + 1AA. Krause, F. Yang
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-nearest neighbor
- Neural networks (backpropagation, regularization, convolutional neural networks)
- Unsupervised learning (k-means, PCA, neural network autoencoders)
- 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 approaches to unsupervised learning (Gaussian mixtures, EM)
LiteraturTextbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Voraussetzungen / BesonderesDesigned to provide a basis for following courses:
- Advanced Machine Learning
- Deep Learning
- Probabilistic Artificial Intelligence
- Seminar "Advanced Topics in Machine Learning"
Physik
NummerTitelTypECTSUmfangDozierende
402-0812-00LComputational Statistical Physics Information W8 KP2V + 2UM. Krstic Marinkovic
KurzbeschreibungSimulationsmethoden in der statistischen Physik. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden, Renormierungsgruppe. Anwendung auf Boltzmann-Maschinen. Simulation von Nichtgleichgewichtssystemen.

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. Im ersten Teil lernen Studenten die folgenden Methoden anzuwenden: Klassische Monte-Carlo-Simulationen, finite-size scaling, Clusteralgorithmen, Histogramm-Methoden, Renormierungsgruppe. Ausserdem lernen Studenten die Anwendung der Methoden aus der Statistischen Physik auf Boltzmann-Maschinen kennen und lernen wie Nichtgleichgewichtssysteme simuliert werden.

Im zweiten Teil wenden die Studenten Methoden zur Simulation von Molekulardynamiken an. Das beinhaltet unter anderem auch langreichweitige Wechselwirkungen, Ewald-Summation und diskrete Elemente.
InhaltSimulationsmethoden in der statistischen Physik. Klassische Monte-Carlo-Simulationen: finite-size scaling, Clusteralgorithmen, Histogramm-Methoden, Renormierungsgruppe. Anwendung auf Boltzmann-Maschinen. Simulation von Nichtgleichgewichtssystemen. Molekulardynamik-Simulationen: langreichweitige Wechselwirkungen, Ewald-Summation, diskrete Elemente, Parallelisierung.
SkriptSkript und Folien sind online verfügbar und werden bei Bedarf verteilt.
LiteraturLiteraturempfehlungen und Referenzen sind im Skript enthalten.
Voraussetzungen / BesonderesGrundlagenwissen in der Statistischen Physik, Klassischen Mechanik und im Bereich der Rechnergestützten Methoden ist empfohlen.
402-0810-00LComputational Quantum Physics
Fachstudierende UZH müssen das Modul PHY522 direkt an der UZH buchen.
W8 KP2V + 2UM. H. Fischer
KurzbeschreibungThis course provides an introduction to simulation methods for quantum systems. Starting from the one-body problem, a special emphasis is on quantum many-body problems, where we cover both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, matrix product states, and quantum Monte Carlo methods).
LernzielThrough lectures and practical programming exercises, after this course:
Students are able to describe the difficulties of quantum mechanical simulations.
Students are able to explain the strengths and weaknesses of the methods covered.
Students are able to select an appropriate method for a given problem.
Students are able to implement basic versions of all algorithms discussed.
SkriptA script for this lecture will be provided.
LiteraturA list of additional references will be provided in the script.
Voraussetzungen / BesonderesA basic knowledge of quantum mechanics, numerical tools (numerical differentiation and integration, linear solvers, eigensolvers, root solvers, optimization), and a programming language (for the teaching assignments, you are free to choose your preferred one).
227-0161-00LMolecular and Materials Modelling Information W4 KP2V + 2UD. Passerone, C. Pignedoli
KurzbeschreibungThe 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.
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, 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.
Inhalt-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
SkriptA script will be made available and complemented by literature references.
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.

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.
Computational Finance
Die Kurse aus diesem Vertiefungsgebiet finden im Herbstsemester statt.
Electromagnetics
NummerTitelTypECTSUmfangDozierende
227-0707-00LOptimization Methods for EngineersW3 KP2GJ. Smajic
KurzbeschreibungErste Semesterhälfte: Einführung in die wichtigsten Methoden der numerischen Optimierung mit Schwerpunkt auf stochastischen 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 skript (39 pages) plus the view graphs are provided
Voraussetzungen / BesonderesVorlesung nur in der 1. Semesterhälfte, Übungen in Form kleiner Projekte in der 2. Semesterhälfte, Präsentation der Resultate in der letzten Semesterwoche.
Geophysik
Empfohlene Kombinationen:
Fach 1 + Fach 2
Fach 1 + Fach 3
Fach 2 + Fach 3
Fach 3 + Fach 4
Fach 5 + Fach 6 + Fach 8
Fach 4 + Fach 5
Fach 7 + Fach 8
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
NummerTitelTypECTSUmfangDozierende
651-4094-00LNumerical Modelling for Applied GeophysicsW5 KP2GJ. Robertsson, H. Maurer
KurzbeschreibungNumerical modelling in environmental and exploration geophysics. The course covers different numerical methods such as finite difference and finite element methods applied to solve PDE’s for instance governing seismic wave propagation and geoelectric problems.

Prerequisites include basic knowledge of (i) signal processing and applied mathematics such as Fourier analysis and (ii) Matlab.
LernzielAfter this course students should have a good overview of numerical modelling techniques commonly used in environmental and exploration geophysics. Students should be familiar with the basic principles of the methods and how they are used to solve real problems. They should know advantages and disadvantages as well as the limitations of the individual approaches.

The course includes exercises in Matlab where the stduents both should lear, understand and use existing scripts as well as carrying out some coding in Matlab themselves.
InhaltDuring the first part of the course, the following topics are covered:
- Applications of modelling
- Physics of acoustic, elastic, viscoelastic wave equations as well as Maxwell's equations for electromagnetic wave propagation and diffusive problems
- Recap of basic techniques in signal processing and applied mathematics
- Potential field modelling
- Solving PDE's, boundary conditions and initial conditions
- Acoustic/elastic wave propagation I, explicit time-domain finite-difference methods
- Acoustic/elastic wave propagation II, Viscoelastic, pseudospectral
- Acoustic/elastic wave propagation III, spectral accuracy in time, frequency domain FD, Eikonal
- Implicit finite-difference methods (geoelectric)
- Finite element methods, 1D/2D (heat equation)
- Finite element methods, 3D (geoelectric)
- Acoustic/elastic wave propagation IV, Finite element and spectral element methods
- HPC and current challenges in computational seismology
- Seismic data imaging project

Most of the lecture 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.
LiteraturIgel, H., 2017. Computational seismology: a practical introduction. Oxford University Press.
Voraussetzungen / BesonderesThis course is offered as a full semester course. During the second part of the semester some lecture slots will be dedicated towards working on exercises and course projects.
Geophysik: Fach 5
findet im Herbstsemester statt
Geophysik: Fach 6
NummerTitelTypECTSUmfangDozierende
651-4006-00LSeismology of the Spherical EarthW3 KP3GM. 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.

Nolet, G., A Breviary of Seismic Tomography, Cambridge University Press, 2008.
Voraussetzungen / BesonderesThis is a quantitative lecture with an emphasis on mathematical description of wave propagation phenomena on the global scale, hence basic knowledge in vector calculus, linear algebra and analysis as well as seismology (e.g. from the 'wave propagation' lecture) are essential to follow this course.
Geophysik: Fach 7
NummerTitelTypECTSUmfangDozierende
651-4096-00LInverse Theory I: BasicsW3 KP2VA. Fichtner
KurzbeschreibungInverse theory is the art of inferring properties of a physical system from noisy and sparse observations. It is used to transform observations of waves into 3D images of a medium seismic tomography, medical imaging and material science; to constrain density in the Earth from gravity; to obtain probabilities of life on exoplanets ... . Inverse theory is at the heart of many natural sciences.
LernzielThe goal of this course is to enable students to develop a mathematical formulation of specific inference (inverse) problems that may arise anywhere in the physical sciences, and to implement suitable solution methods. Furthermore, students should become aware that nearly all relevant inverse problems are ill-posed, and that their meaningful solution requires the addition of prior knowledge in the form of expertise and physical intuition. This is what makes inverse theory an art.
InhaltThis first of two courses covers the basics needed to address (and hopefully solve) any kind of inverse problem. Starting from the description of information in terms of probabilities, we will derive Bayes' Theorem, which forms the mathematical foundation of modern scientific inference. This will allow us to formalise the process of gaining information about a physical system using new observations. Following the conceptual part of the course, we will focus on practical solutions of inverse problems, which will lead us to study Monte Carlo methods and the special case of least-squares inversion.

In more detail, we aim to cover the following main topics:

1. The nature of observations and physical model parameters
2. Representing information by probabilities
3. Bayes' theorem and mathematical scientific inference
4. Random walks and Monte Carlo Methods
5. The Metropolis-Hastings algorithm
6. Simulated Annealing
7. Linear inverse problems and the least-squares method
8. Resolution and the nullspace
9. Basic concepts of iterative nonlinear inversion methods

While the concepts introduced in this course are universal, they will be illustrated with numerous simple and intuitive examples. These will be complemented with a collection of computer and programming exercises.

Prerequisites for this course include (i) basic knowledge of analysis and linear algebra, (ii) basic programming skills, for instance in Matlab or Python, and (iii) scientific curiosity.
SkriptPresentation slides and detailed lecture notes will be provided.
Voraussetzungen / BesonderesThis course is offered as a half-semester course during the first part of the semester
651-4096-02LInverse Theory II: Applications
Voraussetzung: Erfolgreicher Abschluss von 651-4096-00L Inverse Theory I: Basics.
W3 KP2GA. Fichtner, C. Böhm
KurzbeschreibungThis second part of the course on Inverse Theory provides an introduction to the numerical solution of large-scale inverse problems. Specific examples are drawn from different areas of geophysics and image processing. Students solve various model problems using python and jupyter notebooks, and familiarize themselves with relevant open-source libraries and commercial software.
LernzielThis course provides numerical tools and recipes to solve (non)-linear inverse problems arising in nearly all fields of science and engineering. After successful completion of the class, the students will have a thorough understanding of suitable solution algorithms, common challenges and possible mitigations to infer parameters that govern large-scale physical systems from sparse data measurements.

Prerequisites for this course are (i) 651-4096-00L Inverse Theory: Basics, (ii) basic programming skills.
InhaltThe class discusses several important concepts to solve (non)-linear inverse problems and demonstrates how to apply them to real-world data applications. All sessions are split into a lecture part in the first half, followed by tutorials using python and jupyter notebooks in the second. The range of covered topics include:

1. Regularization filters and image deblurring
2. Travel-time tomography
3. Line-search methods
4. Time reversal and Born’s approximation
5. Adjoint methods
6. Full-waveform inversion
SkriptPresentation slides and some background material will be provided.
Voraussetzungen / BesonderesThis course is offered as a half-semester course during the second part of the semester
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