Search result: Catalogue data in Autumn Semester 2023
Computational Science and Engineering Master ![]() | ||||||||||||||||||||||||||||||||||||
![]() 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. | ||||||||||||||||||||||||||||||||||||
Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||
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252-0535-00L | Advanced Machine Learning ![]() | W | 10 credits | 3V + 2U + 4A | J. M. Buhmann | |||||||||||||||||||||||||||||||
Abstract | Machine 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 objective | Students 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. | |||||||||||||||||||||||||||||||||||
Content | The 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 notes | No lecture notes, but slides will be made available on the course webpage. | |||||||||||||||||||||||||||||||||||
Literature | C. 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 / Notice | The 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. | |||||||||||||||||||||||||||||||||||
401-4671-00L | Advanced Numerical Methods for CSE ![]() | W | 9 credits | 4V + 2U + 1P | R. Hiptmair | |||||||||||||||||||||||||||||||
Abstract | This 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. | |||||||||||||||||||||||||||||||||||
Content | I. Local low-rank compression II. Convolution quadrature III. (Algebraic) multigrid methods IV. Approximation, interpolation, and quadrature in high dimensions | |||||||||||||||||||||||||||||||||||
Lecture notes | Lecture 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"). | |||||||||||||||||||||||||||||||||||
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![]() 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). | ||||||||||||||||||||||||||||||||||||
Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||
263-5210-00L | Probabilistic Artificial Intelligence ![]() ![]() | W | 8 credits | 3V + 2U + 2A | A. Krause | |||||||||||||||||||||||||||||||
Abstract | This 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 objective | How 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. | |||||||||||||||||||||||||||||||||||
Content | Topics 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 / Notice | Solid basic knowledge in statistics, algorithms and programming. The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite. | |||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||
401-7851-00L | Theoretical 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 | W | 10 credits | 4V + 2U | University lecturers | |||||||||||||||||||||||||||||||
Abstract | This 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. | |||||||||||||||||||||||||||||||||||
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Content | This 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. | |||||||||||||||||||||||||||||||||||
Literature | Course 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 / Notice | This 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 | |||||||||||||||||||||||||||||||||||
401-7855-00L | Computational 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 | W | 6 credits | 2V | L. M. Mayer | |||||||||||||||||||||||||||||||
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Learning objective | Acquire 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 | |||||||||||||||||||||||||||||||||||
Content | 1. 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 | |||||||||||||||||||||||||||||||||||
Literature | Galactic 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 / Notice | Some 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 | |||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||
701-0023-00L | Atmosphere ![]() | W | 3 credits | 2V | E. Fischer, T. Peter | |||||||||||||||||||||||||||||||
Abstract | Basic principles of the atmosphere, physical structure and chemical composition, trace gases, atmospheric cycles, circulation, stability, radiation, condensation, clouds, oxidation capacity and ozone layer. | |||||||||||||||||||||||||||||||||||
Learning objective | Understanding 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. | |||||||||||||||||||||||||||||||||||
Content | Basic principles of the atmosphere, physical structure and chemical composition, trace gases, atmospheric cycles, circulation, stability, radiation, condensation, clouds, oxidation capacity and ozone layer. | |||||||||||||||||||||||||||||||||||
Lecture notes | Written 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. | |||||||||||||||||||||||||||||||||||
651-4053-05L | Boundary Layer Meteorology | W | 4 credits | 3G | M. Rotach, P. Calanca | |||||||||||||||||||||||||||||||
Abstract | The 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 objective | Students 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 notes | available (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 / Notice | Umwelt-Fluiddynamik (701-0479-00L) (environment fluid dynamics) or equivalent and basic knowledge in atmospheric science | |||||||||||||||||||||||||||||||||||
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701-1221-00L | Dynamics of Large-Scale Atmospheric Flow ![]() | W | 4 credits | 2V + 1U | H. Wernli, J. Riboldi | |||||||||||||||||||||||||||||||
Abstract | This 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 objective | Understanding of dynamic processes of large-scale atmospheric flow and their mathematical-physical formulation. | |||||||||||||||||||||||||||||||||||
Content | Dynamical 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 notes | Dynamics 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 / Notice | Physics I, II, Environmental Fluid Dynamics | |||||||||||||||||||||||||||||||||||
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401-5930-00L | Seminar in Physics of the Atmosphere for CSE ![]() | W | 4 credits | 2S | H. Joos, C. Schär | |||||||||||||||||||||||||||||||
Abstract | The 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 | |||||||||||||||||||||||||||||||||||
Content | n 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 / Notice | In 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. | |||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||
529-0004-01L | Classical Simulation of (Bio)Molecular Systems ![]() | W | 6 credits | 4G | P. H. Hünenberger, J. Dolenc, S. Riniker | |||||||||||||||||||||||||||||||
Abstract | Molecular 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 objective | Introduction to classical (atomistic) computer simulation of (bio)molecular systems, development of skills to carry out and interpret these simulations. | |||||||||||||||||||||||||||||||||||
Content | Molecular 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 notes | The powerpoint slides of the lectures will be made available weekly on the website in pdf format (on the day preceding each lecture). | |||||||||||||||||||||||||||||||||||
Literature | See: www.csms.ethz.ch/education/CSBMS | |||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Since 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 | |||||||||||||||||||||||||||||||||||
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529-0003-01L | Advanced Quantum Chemistry | W | 6 credits | 3G | M. Reiher, T. Weymuth | |||||||||||||||||||||||||||||||
Abstract | Advanced, 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 objective | The 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. | |||||||||||||||||||||||||||||||||||
Content | 1) 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 notes | A set of detailed lecture notes will be provided, which will cover the whole course. | |||||||||||||||||||||||||||||||||||
Literature | 1) 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 / Notice | Strongly recommended (preparatory) courses are: quantum mechanics and quantum chemistry | |||||||||||||||||||||||||||||||||||
401-5940-00L | Seminar in Chemistry for CSE ![]() | W | 4 credits | 2S | P. H. Hünenberger, M. Reiher | |||||||||||||||||||||||||||||||
Abstract | The 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 | |||||||||||||||||||||||||||||||||||
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![]() ![]() 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. | ||||||||||||||||||||||||||||||||||||
Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||
151-0103-00L | Fluid Dynamics II | O | 3 credits | 2V + 1U | P. Jenny | |||||||||||||||||||||||||||||||
Abstract | Two-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 objective | Expand basic knowledge of fluid dynamics. Concepts, phenomena and quantitative description of irrotational (potential), rotational, and one-dimensional compressible flows. | |||||||||||||||||||||||||||||||||||
Content | Two-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 notes | Lecture notes are available (in German). (See also info on literature below.) | |||||||||||||||||||||||||||||||||||
Literature | Relevant 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 / Notice | Analysis I/II, Knowledge of Fluid Dynamics I, thermodynamics of ideal gas | |||||||||||||||||||||||||||||||||||
151-0109-00L | Turbulent Flows | W | 4 credits | 2V + 1U | P. Jenny | |||||||||||||||||||||||||||||||
Abstract | Contents - 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 objective | Basic 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 notes | Lecture notes are available | |||||||||||||||||||||||||||||||||||
Literature | S.B. Pope, Turbulent Flows, Cambridge University Press, 2000 | |||||||||||||||||||||||||||||||||||
151-0532-00L | Nonlinear Dynamics and Chaos I | W | 4 credits | 2V + 2U | G. Haller | |||||||||||||||||||||||||||||||
Abstract | Basic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics. | |||||||||||||||||||||||||||||||||||
Learning objective | This 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 notes | The 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. | |||||||||||||||||||||||||||||||||||
151-0213-00L | Fluid Dynamics with the Lattice Boltzmann Method Does not take place this semester. | W | 4 credits | 3G | I. Karlin | |||||||||||||||||||||||||||||||
Abstract | The course provides an introduction to theoretical foundations and practical usage of the Lattice Boltzmann Method for fluid dynamics simulations. | |||||||||||||||||||||||||||||||||||
Learning objective | Methods 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. | |||||||||||||||||||||||||||||||||||
Content | The 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 notes | Lecture 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 / Notice | The course addresses mainly graduate students (MSc/Ph D) but BSc students can also attend. | |||||||||||||||||||||||||||||||||||
151-0709-00L | Stochastic Methods for Engineers and Natural Scientists | W | 4 credits | 4G | D. W. Meyer-Massetti | |||||||||||||||||||||||||||||||
Abstract | The 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 objective | By 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 notes | Detailed lecture notes will be provided. | |||||||||||||||||||||||||||||||||||
Literature | Some 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 | |||||||||||||||||||||||||||||||||||
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151-0125-00L | Hydrodynamics and Cavitation Does not take place this semester. | W | 4 credits | 3G | O. Supponen | |||||||||||||||||||||||||||||||
Abstract | This course builds on the foundations of fluid dynamics to describe hydrodynamic flows and provides an introduction to cavitation. | |||||||||||||||||||||||||||||||||||
Learning objective | The 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. | |||||||||||||||||||||||||||||||||||
Content | The 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 notes | Class notes and handouts | |||||||||||||||||||||||||||||||||||
Literature | Literature will be provided in the course material. | |||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Fluid dynamics I & II or equivalent | |||||||||||||||||||||||||||||||||||
401-5950-00L | Seminar in Fluid Dynamics for CSE ![]() | W | 4 credits | 2S | P. Jenny | |||||||||||||||||||||||||||||||
Abstract | Enlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics | |||||||||||||||||||||||||||||||||||
Learning objective | Enlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics | |||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Contact Prof. P. Jenny before the beginning of the semester | |||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||
227-0103-00L | Control Systems ![]() | W | 6 credits | 2V + 2U | F. Dörfler | |||||||||||||||||||||||||||||||
Abstract | Study 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 objective | Study 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. | |||||||||||||||||||||||||||||||||||
Content | Process 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. | |||||||||||||||||||||||||||||||||||
Literature | K. 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 / Notice | Prerequisites: Signal and Systems Theory II. MATLAB is used for system analysis and simulation. |
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