Search result: Catalogue data in Autumn Semester 2023

Computational Science and Engineering Master Information
Core Courses
In the ‘core courses’ subcategory, at least two course units must be successfully completed.

Only one of the two course units
263-5210-00L Probabilistic Artificial Intelligence resp.
252-0535-00L Advanced Machine Learning
may be recognised for credits as a core course. However, the other course unit may be recognised for a different category.
NumberTitleTypeECTSHoursLecturers
252-0535-00LAdvanced Machine Learning Information W10 credits3V + 2U + 4AJ. M. Buhmann
AbstractMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
Learning objectiveStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
ContentThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
Lecture notesNo lecture notes, but slides will be made available on the course webpage.
LiteratureC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Prerequisites / NoticeThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
401-4671-00LAdvanced Numerical Methods for CSE Information W9 credits4V + 2U + 1PR. Hiptmair
AbstractThis course will focus on teaching different advanced topics in numerical methods for science and engineering. The main aim would be introduce novel algorithms and discuss their implementation.
Learning objective* Understanding the mathematical foundations and design principles of a selection of modern numerical methods for challenging problems.

* Ability to adapt the presented paradigms and algorithms to modified or new problems arising from applications in computational science and engineering.

* Ability to judge the scope, strengths and weaknesses of the numerical methods covered in this course and of methods derived from them.

* Skills in translating a high-level description of an algorithm into efficient code.
ContentI. Local low-rank compression
II. Convolution quadrature
III. (Algebraic) multigrid methods
IV. Approximation, interpolation, and quadrature in high dimensions
Lecture notesLecture material will be created during the course and will be made available.
Prerequisites / Notice- Familiarity with basic numerical methods
(as taught in the course "Numerical Methods for CSE").
- Knowledge of numerical methods for differential equations (as covered in the course "Numerical Methods for Partial Differential Equations").
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Project Managementfostered
Personal CompetenciesCritical Thinkingfostered
Core Courses (continued)
Only one of the two course units
263-5210-00L Probabilistic Artificial Intelligence resp.
252-0535-00L Advanced Machine Learning
may be recognised for credits as a core course. However, the other course unit may be recognised for a different category.
For the category assignment take contact with the Study Administration (www.math.ethz.ch/studiensekretariat).
NumberTitleTypeECTSHoursLecturers
263-5210-00LProbabilistic Artificial Intelligence Information Restricted registration - show details W8 credits3V + 2U + 2AA. Krause
AbstractThis course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics.
Learning objectiveHow can we build systems that perform well in uncertain environments? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as robotics. The course is designed for graduate students.
ContentTopics covered:
- Probability
- Probabilistic inference (variational inference, MCMC)
- Bayesian learning (Gaussian processes, Bayesian deep learning)
- Probabilistic planning (MDPs, POMPDPs)
- Multi-armed bandits and Bayesian optimization
- Reinforcement learning
Prerequisites / NoticeSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
Fields of Specialization
Astrophysics
NumberTitleTypeECTSHoursLecturers
401-7851-00LTheoretical Astrophysics (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: AST512

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W10 credits4V + 2UUniversity lecturers
AbstractThis course covers the foundations of astrophysical fluid dynamics, the Boltzmann equation, equilibrium systems and their stability, the structure of stars, astrophysical turbulence, accretion disks and their stability, the foundations of radiative transfer, collisionless systems, the structure and stability of dark matter halos and stellar galactic disks.
Learning objective
ContentThis course covers the foundations of astrophysical fluid dynamics, the theory of collisions and the Boltzmann equation, the notion of equilibrium systems and their stability, the structure of stars, the theory of astrophysical turbulence, the theory of accretion disks and their stability, the foundations of astrophysical radiative transfer, the theory of collisionless system, the structure and stability of dark matter halos and stellar galactic disks.
LiteratureCourse Materials:
1- The Physics of Astrophysics, Volume 1: Radiation by Frank H. Shu
2- The Physics of Astrophysics, Volume 2: Gas Dynamics by Frank H. Shu
3- Foundations of radiation hydrodynamics, Dimitri Mihalas and Barbara Weibel-Mihalas
4- Radiative Processes in Astrophysics, George B. Rybicki and Alan P. Lightman
5- Galactic Dynamics, James Binney and Scott Tremaine
Prerequisites / NoticeThis is a full black board ad chalk experience for students with a strong background in mathematics and physics.

Prerequisites:
Introduction to Astrophysics
Mathematical Methods for the Physicist
Quantum Mechanics
(All preferred but not obligatory)

Prior Knowledge:
Mechanics
Quantum Mechanics and atomic physics
Thermodynamics
Fluid Dynamics
Electrodynamics
401-7855-00LComputational Astrophysics (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: AST245

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W6 credits2VL. M. Mayer
Abstract
Learning objectiveAcquire knowledge of main methodologies for computer-based models of astrophysical systems,the physical equations behind them, and train such knowledge with simple examples of computer programmes
Content1. Integration of ODE, Hamiltonians and Symplectic integration techniques, time adaptivity, time reversibility
2. Large-N gravity calculation, collisionless N-body systems and their simulation
3. Fast Fourier Transform and spectral methods in general
4. Eulerian Hydrodynamics: Upwinding, Riemann solvers, Limiters
5. Lagrangian Hydrodynamics: The SPH method
6. Resolution and instabilities in Hydrodynamics
7. Initial Conditions: Cosmological Simulations and Astrophysical Disks
8. Physical Approximations and Methods for Radiative Transfer in Astrophysics
LiteratureGalactic Dynamics (Binney & Tremaine, Princeton University Press),
Computer Simulation using Particles (Hockney & Eastwood CRC press),
Targeted journal reviews on computational methods for astrophysical fluids (SPH, AMR, moving mesh)
Prerequisites / NoticeSome knowledge of UNIX, scripting languages (see www.physik.uzh.ch/lectures/informatik/python/ as an example), some prior experience programming, knowledge of C, C++ beneficial
Physics of the Atmosphere
NumberTitleTypeECTSHoursLecturers
701-0023-00LAtmosphere Information W3 credits2VE. Fischer, T. Peter
AbstractBasic principles of the atmosphere, physical structure and chemical composition, trace gases, atmospheric cycles, circulation, stability, radiation, condensation, clouds, oxidation capacity and ozone layer.
Learning objectiveUnderstanding of basic physical and chemical processes in the atmosphere. Understanding of mechanisms of and interactions between: weather - climate, atmosphere - ocean - continents, troposhere - stratosphere. Understanding of environmentally relevant structures and processes on vastly differing scales. Basis for the modelling of complex interrelations in the atmospehre.
ContentBasic principles of the atmosphere, physical structure and chemical composition, trace gases, atmospheric cycles, circulation, stability, radiation, condensation, clouds, oxidation capacity and ozone layer.
Lecture notesWritten information will be supplied.
Literature- John H. Seinfeld and Spyros N. Pandis, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, Wiley, New York, 1998.
- Gösta H. Liljequist, Allgemeine Meteorologie, Vieweg, Braunschweig, 1974.
651-4053-05LBoundary Layer MeteorologyW4 credits3GM. Rotach, P. Calanca
AbstractThe Planetary Boundary Layer (PBL) constitutes the lower part of the atmosphere, is characterized by turbulent mixing and ensures the exchange of energy, mass and momentum between the Earth’s surface and the atmosphere. The course provides the theoretical background for understanding the structure and dynamics of the PBL. Idealized concepts are reviewed and contrasted to real world applications.
Learning objectiveStudents are able to:
- Name the basic approaches needed to describe planetary boundary layer flows and associated turbulent exchange processes.
- Apply these concepts to answer comprehension questions and solve simple problems related to the structure and dynamics of the PBL.
- Independently judge the applicability of learned concepts and tools to real-world situations.
Content- Introduction
- PBL structure and stability
- Turbulence and turbulent transport
- Scaling and similarity theory
- Spectral characteristics
- Conservation equations in a turbulent flow
- Closure problem and closure assumptions
- Rough surfaces and the roughness sublayer
- Complex terrain
Lecture notesavailable (i.e. in English)
Literature- Stull, R.B.: 1988, "An Introduction to Boundary Layer Meteorology", (Kluwer), 666 pp.
- Panofsky, H. A. and Dutton, J.A.: 1984, "Atmospheric Turbulence, Models and Methods for Engineering Applications", (J. Wiley), 397 pp.
- Kaimal JC and Finningan JJ: 1994, Atmospheric Boundary Layer Flows, Oxford University Press, 289 pp.
- Wyngaard JC: 2010, Turbulence in the Atmosphere, Cambridge University Press, 393pp.
Prerequisites / NoticeUmwelt-Fluiddynamik (701-0479-00L) (environment fluid dynamics) or equivalent and basic knowledge in atmospheric science
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Problem-solvingfostered
Personal CompetenciesCreative Thinkingfostered
701-1221-00LDynamics of Large-Scale Atmospheric Flow Information W4 credits2V + 1UH. Wernli, J. Riboldi
AbstractThis lecture course is about the fundamental aspects of the dynamics of extratropical weather systems (quasi-geostropic dynamics, potential vorticity, Rossby waves, baroclinic instability). The fundamental concepts are formally introduced, quantitatively applied and illustrated with examples from the real atmosphere. Exercises (quantitative and qualitative) form an essential part of the course.
Learning objectiveUnderstanding of dynamic processes of large-scale atmospheric flow and their mathematical-physical formulation.
ContentDynamical Meteorology is concerned with the dynamical processes of the
earth's atmosphere. The fundamental equations of motion in the atmosphere will be discussed along with the dynamics and interactions of synoptic system - i.e. the low and high pressure systems that determine our weather. The motion of such systems can be understood in terms of quasi-geostrophic theory. The lecture course provides a derivation of the mathematical basis along with some interpretations and applications of the concept.
Lecture notesDynamics of large-scale atmospheric flow
Literature- Holton J.R., An introduction to Dynamic Meteorogy. Academic Press, fourth edition 2004,
- Pichler H., Dynamik der Atmosphäre, Bibliographisches Institut, 456 pp. 1997
Prerequisites / NoticePhysics I, II, Environmental Fluid Dynamics
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Social CompetenciesCooperation and Teamworkfostered
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
401-5930-00LSeminar in Physics of the Atmosphere for CSE Information W4 credits2SH. Joos, C. Schär
AbstractThe process of writing a scientific proposal is
introduced and the essential elements, including the peer
review process, are outlined and class exercises train
scientific writing skills. Knowledge exchange between class
participants is promoted through the preparation of a master thesis
proposal and evaluation of each other's work. An introduction to presentation skills is provided.
Learning objective- scientific writing
- introduction to peer review process
- correction / feedback to the proposals of other participants
- presentation skills
Contentn this seminar, the process of writing a scientific proposal is
introduced. The essential elements of a proposal, including the peer
review process, are outlined and class exercises train
scientific writing skills. Knowledge exchange between class
participants is promoted through the preparation of a master thesis
proposal and evaluation of each other's work. Furthermore, an introduction to presentation skills is provided.
Prerequisites / NoticeIn this seminar it is mandatory to write a proposal about an upcoming MSc thesis or semester project. If no such project is planned, this Seminar cannot be taken. Please contact the lecturers (hanna.joos@env.ethz.ch) on time if you plan to take this seminar.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesfostered
Method-specific CompetenciesDecision-makingfostered
Project Managementfostered
Social CompetenciesCommunicationfostered
Personal CompetenciesSelf-awareness and Self-reflection fostered
Self-direction and Self-management fostered
Chemistry
NumberTitleTypeECTSHoursLecturers
529-0004-01LClassical Simulation of (Bio)Molecular Systems Information W6 credits4GP. H. Hünenberger, J. Dolenc, S. Riniker
AbstractMolecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS).
Learning objectiveIntroduction to classical (atomistic) computer simulation of (bio)molecular systems, development of skills to carry out and interpret these simulations.
ContentMolecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS).
Lecture notesThe powerpoint slides of the lectures will be made available weekly on the website in pdf format (on the day preceding each lecture).
LiteratureSee: www.csms.ethz.ch/education/CSBMS
Prerequisites / NoticeSince the exercises on the computer do convey and test essentially different skills than those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam (learning component, possible bonus of up to 0.25 points on the exam mark).

For more information about the lecture: www.csms.ethz.ch/education/CSBMS
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Personal CompetenciesAdaptability and Flexibilityfostered
Creative Thinkingfostered
529-0003-01LAdvanced Quantum ChemistryW6 credits3GM. Reiher, T. Weymuth
AbstractAdvanced, but fundamental topics central to the understanding of theory in chemistry and for solving actual chemical problems with a computer.
Examples are:
* Operators derived from principles of relativistic quantum mechanics
* Relativistic effects + methods of relativistic quantum chemistry
* Open-shell molecules + spin-density functional theory
* New electron-correlation theories
Learning objectiveThe aim of the course is to provide an in-depth knowledge of theory and method development in theoretical chemistry. It will be shown that this is necessary in order to be able to solve actual chemical problems on a computer with quantum chemical methods.

The relativistic re-derivation of all concepts known from (nonrelativistic) quantum mechanics and quantum-chemistry lectures will finally explain the form of all operators in the molecular Hamiltonian - usually postulated rather than deduced. From this, we derive operators needed for molecular spectroscopy (like those required by magnetic resonance spectroscopy). Implications of other assumptions in standard non-relativistic quantum chemistry shall be analyzed and understood, too. Examples are the Born-Oppenheimer approximation and the expansion of the electronic wave function in a set of pre-defined many-electron basis functions (Slater determinants). Overcoming these concepts, which are so natural to the theory of chemistry, will provide deeper insights into many-particle quantum mechanics. Also revisiting the workhorse of quantum chemistry, namely density functional theory, with an emphasis on open-shell electronic structures (radicals, transition-metal complexes) will contribute to this endeavor. It will be shown how these insights allow us to make more accurate predictions in chemistry in practice - at the frontier of research in theoretical chemistry.
Content1) Introductory lecture: basics of quantum mechanics and quantum chemistry
2) Einstein's special theory of relativity and the (classical) electromagnetic interaction of two charged particles
3) Klein-Gordon and Dirac equation; the Dirac hydrogen atom
4) Numerical methods based on the Dirac-Fock-Coulomb Hamiltonian, two-component and scalar relativistic Hamiltonians
5) Response theory and molecular properties, derivation of property operators, Breit-Pauli-Hamiltonian
6) Relativistic effects in chemistry and the emergence of spin
7) Spin in density functional theory
8) New electron-correlation theories: Tensor network and matrix product states, the density matrix renormalization group
9) Quantum chemistry without the Born-Oppenheimer approximation
Lecture notesA set of detailed lecture notes will be provided, which will cover the whole course.
Literature1) M. Reiher, A. Wolf, Relativistic Quantum Chemistry, Wiley-VCH, 2014, 2nd edition
2) F. Schwabl: Quantenmechanik für Fortgeschrittene (QM II), Springer-Verlag, 1997
[english version available: F. Schwabl, Advanced Quantum Mechanics]
3) R. McWeeny: Methods of Molecular Quantum Mechanics, Academic Press, 1992
4) C. R. Jacob, M. Reiher, Spin in Density-Functional Theory, Int. J. Quantum Chem. 112 (2012) 3661
http://onlinelibrary.wiley.com/doi/10.1002/qua.24309/abstract
5) K. H. Marti, M. Reiher, New Electron Correlation Theories for Transition Metal Chemistry, Phys. Chem. Chem. Phys. 13 (2011) 6750
http://pubs.rsc.org/en/Content/ArticleLanding/2011/CP/c0cp01883j
6) K.H. Marti, M. Reiher, The Density Matrix Renormalization Group Algorithm in Quantum Chemistry, Z. Phys. Chem. 224 (2010) 583
http://www.oldenbourg-link.com/doi/abs/10.1524/zpch.2010.6125
7) E. Mátyus, J. Hutter, U. Müller-Herold, M. Reiher, On the emergence of molecular structure, Phys. Rev. A 83 2011, 052512
http://pra.aps.org/abstract/PRA/v83/i5/e052512

Note also the standard textbooks:
A) A. Szabo, N.S. Ostlund. Verlag, Dover Publications
B) I. N. Levine, Quantum Chemistry, Pearson
C) T. Helgaker, P. Jorgensen, J. Olsen: Molecular Electronic-Structure Theory, Wiley, 2000
D) R.G. Parr, W. Yang: Density-Functional Theory of Atoms and Molecules, Oxford University Press, 1994
E) R.M. Dreizler, E.K.U. Gross: Density Functional Theory, Springer-Verlag, 1990
Prerequisites / NoticeStrongly recommended (preparatory) courses are: quantum mechanics and quantum chemistry
401-5940-00LSeminar in Chemistry for CSE Information W4 credits2SP. H. Hünenberger, M. Reiher
AbstractThe student will carry out a literature study on a topic of his or her liking (suggested by or in agreement with the supervisor) in the area of computer simulation in chemistry (Prof. Hünenberger) or of quantum chemistry (Prof. Reiher), the results of which are to be presented both orally and in written form.

For more information:
http://www.csms.ethz.ch/education/CSE_seminar.html
Learning objective
Fluid Dynamics
One of the course units
151-0103-00L Fluid Dynamics II
151-0109-00L Turbulent Flows
is compulsory.
Students able to follow courses in German are advised to choose 151-0103-00L Fluid Dynamics II.
NumberTitleTypeECTSHoursLecturers
151-0103-00LFluid Dynamics IIO3 credits2V + 1UP. Jenny
AbstractTwo-dimensional irrotational (potential) flows: stream function and potential, singularity method, unsteady flow, aerodynamic concepts.
Vorticity dynamics: vorticity and circulation, vorticity equation, vortex theorems of Helmholtz and Kelvin.
Compressible flows: isentropic flow along stream tube, normal and oblique shocks, Laval nozzle, Prandtl-Meyer expansion, viscous effects.
Learning objectiveExpand basic knowledge of fluid dynamics.
Concepts, phenomena and quantitative description of irrotational (potential), rotational, and one-dimensional compressible flows.
ContentTwo-dimensional irrotational (potential) flows: stream function and potential, complex notation, singularity method, unsteady flow, aerodynamic concepts.
Vorticity dynamics: vorticity and circulation, vorticity equation, vortex theorems of Helmholtz and Kelvin.
Compressible flows: isentropic flow along stream tube, normal and oblique shocks, Laval nozzle, Prandtl-Meyer expansion, viscous effects.
Lecture notesLecture notes are available (in German).
(See also info on literature below.)
LiteratureRelevant chapters (corresponding to lecture notes) from the textbook

P.K. Kundu, I.M. Cohen, D.R. Dowling: Fluid Mechanics, Academic Press, 5th ed., 2011 (includes a free copy of the DVD "Multimedia Fluid Mechanics")

P.K. Kundu, I.M. Cohen, D.R. Dowling: Fluid Mechanics, Academic Press, 6th ed., 2015 (does NOT include a free copy of the DVD "Multimedia Fluid Mechanics")
Prerequisites / NoticeAnalysis I/II, Knowledge of Fluid Dynamics I, thermodynamics of ideal gas
151-0109-00LTurbulent FlowsW4 credits2V + 1UP. Jenny
AbstractContents
- Laminar and turbulent flows, instability and origin of turbulence - Statistical description: averaging, turbulent energy, dissipation, closure problem - Scalings. Homogeneous isotropic turbulence, correlations, Fourier representation, energy spectrum - Free turbulence: wake, jet, mixing layer - Wall turbulence: Channel and boundary layer - Computation and modelling of turbulent flows
Learning objectiveBasic physical phenomena of turbulent flows, quantitative and statistical description, basic and averaged equations, principles of turbulent flow computation and elements of turbulence modelling
Content- Properties of laminar, transitional and turbulent flows.
- Origin and control of turbulence. Instability and transition.
- Statistical description, averaging, equations for mean and fluctuating quantities, closure problem.
- Scalings, homogeneous isotropic turbulence, energy spectrum.
- Turbulent free shear flows. Jet, wake, mixing layer.
- Wall-bounded turbulent flows.
- Turbulent flow computation and modeling.
Lecture notesLecture notes are available
LiteratureS.B. Pope, Turbulent Flows, Cambridge University Press, 2000
151-0532-00LNonlinear Dynamics and Chaos IW4 credits2V + 2UG. Haller
AbstractBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
Learning objectiveThis course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.
Content(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.

(2) Near equilibrium dynamics: Linear and Lyapunov stability

(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations

(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.

(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
Lecture notesThe class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.
Prerequisites / Notice- Prerequisites: Analysis, linear algebra and a basic course in differential equations.

- Exam: two-hour written exam in English.

- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
151-0213-00LFluid Dynamics with the Lattice Boltzmann Method
Does not take place this semester.
W4 credits3GI. Karlin
AbstractThe course provides an introduction to theoretical foundations and practical usage of the Lattice Boltzmann Method for fluid dynamics simulations.
Learning objectiveMethods like molecular dynamics, DSMC, lattice Boltzmann etc are being increasingly used by engineers all over and these methods require knowledge of kinetic theory and statistical mechanics which are traditionally not taught at engineering departments. The goal of this course is to give an introduction to ideas of kinetic theory and non-equilibrium thermodynamics with a focus on developing simulation algorithms and their realizations.

During the course, students will be able to develop a lattice Boltzmann code on their own. Practical issues about implementation and performance on parallel machines will be demonstrated hands on.

Central element of the course is the completion of a lattice Boltzmann code (using the framework specifically designed for this course).

The course will also include a review of topics of current interest in various fields of fluid dynamics, such as multiphase flows, reactive flows, microflows among others.

Optionally, we offer an opportunity to complete a project of student's choice as an alternative to the oral exam. Samples of projects completed by previous students will be made available.
ContentThe course builds upon three parts:
I Elementary kinetic theory and lattice Boltzmann simulations introduced on simple examples.
II Theoretical basis of statistical mechanics and kinetic equations.
III Lattice Boltzmann method for real-world applications.

The content of the course includes:

1. Background: Elements of statistical mechanics and kinetic theory:
Particle's distribution function, Liouville equation, entropy, ensembles; Kinetic theory: Boltzmann equation for rarefied gas, H-theorem, hydrodynamic limit and derivation of Navier-Stokes equations, Chapman-Enskog method, Grad method, boundary conditions; mean-field interactions, Vlasov equation;
Kinetic models: BGK model, generalized BGK model for mixtures, chemical reactions and other fluids.

2. Basics of the Lattice Boltzmann Method and Simulations:
Minimal kinetic models: lattice Boltzmann method for single-component fluid, discretization of velocity space, time-space discretization, boundary conditions, forcing, thermal models, mixtures.

3. Hands on:
Development of the basic lattice Boltzmann code and its validation on standard benchmarks (Taylor-Green vortex, lid-driven cavity flow etc).

4. Practical issues of LBM for fluid dynamics simulations:
Lattice Boltzmann simulations of turbulent flows;
numerical stability and accuracy.

5. Microflow:
Rarefaction effects in moderately dilute gases; Boundary conditions, exact solutions to Couette and Poiseuille flows; micro-channel simulations.

6. Advanced lattice Boltzmann methods:
Entropic lattice Boltzmann scheme, subgrid simulations at high Reynolds numbers; Boundary conditions for complex geometries.

7. Introduction to LB models beyond hydrodynamics:
Relativistic fluid dynamics; flows with phase transitions.
Lecture notesLecture notes on the theoretical parts of the course will be made available.
Selected original and review papers are provided for some of the lectures on advanced topics.
Handouts and basic code framework for implementation of the lattice Boltzmann models will be provided.
Prerequisites / NoticeThe course addresses mainly graduate students (MSc/Ph D) but BSc students can also attend.
151-0709-00LStochastic Methods for Engineers and Natural ScientistsW4 credits4GD. W. Meyer-Massetti
AbstractThe course provides an introduction into stochastic methods that are applicable for example for the description and modeling of turbulent and subsurface flows. Moreover, mathematical techniques are presented that are used to quantify uncertainty in various engineering applications.
Learning objectiveBy the end of the course you should be able to mathematically describe random quantities and their effect on physical systems. Moreover, you should be able to develop basic stochastic models of such systems.
Content- Probability theory, single and multiple random variables, mappings of random variables
- Estimation of statistical moments and probability densities based on data
- Stochastic differential equations, Ito calculus, PDF evolution equations
- Monte Carlo integration with importance and stratified sampling
- Markov-chain Monte Carlo sampling
- Control-variate and multi-level Monte Carlo estimation
- Statistical tests for means and goodness-of-fit
All topics are illustrated with engineering applications.
Lecture notesDetailed lecture notes will be provided.
LiteratureSome textbooks related to the material covered in the course:
Stochastic Methods: A Handbook for the Natural and Social Sciences, Crispin Gardiner, Springer, 2010
The Fokker-Planck Equation: Methods of Solutions and Applications, Hannes Risken, Springer, 1996
Turbulent Flows, S.B. Pope, Cambridge University Press, 2000
Spectral Methods for Uncertainty Quantification, O.P. Le Maitre and O.M. Knio, Springer, 2010
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesassessed
Problem-solvingassessed
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsassessed
Self-direction and Self-management assessed
151-0125-00LHydrodynamics and Cavitation
Does not take place this semester.
W4 credits3GO. Supponen
AbstractThis course builds on the foundations of fluid dynamics to describe hydrodynamic flows and provides an introduction to cavitation.
Learning objectiveThe main learning objectives of this course are:
1. Identify and describe dominant effects in liquid fluid flows through physical modelling.
2. Identify hydrodynamic instabilities and discuss the stability region
3. Describe fragmentation of liquids
4. Explain tension, nucleation and phase-change in liquids.
5. Describe hydrodynamic cavitation and its consequences in physical terms.
6. Recognise experimental techniques and industrial and medical applications for cavitation.
ContentThe course gives an overview on the following topics: hydrostatics, capillarity, hydrodynamic instabilities, fragmentation. Tension in liquids, phase change. Cavitation: single bubbles (nucleation, dynamics, collapse), cavitating flows (attached, cloud, vortex cavitation). Industrial applications and measurement techniques.
Lecture notesClass notes and handouts
LiteratureLiterature will be provided in the course material.
Prerequisites / NoticeFluid dynamics I & II or equivalent
401-5950-00LSeminar in Fluid Dynamics for CSE Restricted registration - show details W4 credits2SP. Jenny
AbstractEnlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics
Learning objectiveEnlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics
Prerequisites / NoticeContact Prof. P. Jenny before the beginning of the semester
Systems and Control
NumberTitleTypeECTSHoursLecturers
227-0103-00LControl Systems Information W6 credits2V + 2UF. Dörfler
AbstractStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
Learning objectiveStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
ContentProcess automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.
LiteratureK. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010.
R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007.
G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010.
J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014.
J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.
Prerequisites / NoticePrerequisites: Signal and Systems Theory II.

MATLAB is used for system analysis and simulation.
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