# Search result: Catalogue data in Autumn Semester 2016

Computational Science and Engineering Master | ||||||

Fields of Specialization | ||||||

Astrophysics | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |
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401-7851-00L | Theoretical Astrophysics (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: AST512 Mind the enrolment deadlines at UZH: http://www.uzh.ch/studies/application/mobilitaet_en.html | W | 10 credits | 4V + 2U | R. Teyssier | |

Abstract | Radiative processes in the interstellar medium; stellar structure and evolution; supernovae; white dwarfs; neutron stars; black holes; planet formation | |||||

Objective | ||||||

Literature | (1) "Formation of stars" (S. Stahler and F. Palla - Wiley editions, this is the book on which about half of the classes will be based and photocopies will be organized during first lecture) (2) "Radiative processes in astrophysics" (R. Ribycki and A. Lightman) (3) "The Physics of Stars" (A.C. Philllips) (4) "Black Holes, White Dwarfs and Neutron Stars: The physics of compact objects" (S. Shapiro and S.A. Teukolski). Additionally PowerPoint slides will be prepared by the lecturer on these and extra topics (e.g. planet formation). | |||||

Prerequisites / Notice | Prerequisites: Elementary atomic physics, thermodynamics, mechanics, fluid dynamics. Introduction to astrophysics (preferred but not obligatory). | |||||

401-7855-00L | Computational Astrophysics (University of Zurich)No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: AST245 Mind the enrolment deadlines at UZH: http://www.uzh.ch/studies/application/mobilitaet_en.html | W | 6 credits | 2V | L. M. Mayer | |

Abstract | 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 | |||||

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 | |||||

Physics of the Atmosphere | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

701-0023-00L | Atmosphere | W | 3 credits | 2V | H. Wernli, E. M. 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. | |||||

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 interface between the atmosphere and the Earth's surface. Theory on transport processes in the PBL and their dynamics is provided. This course treats theoretical background and idealized concepts. These are contrasted to real world applications and current research issues. | |||||

Objective | Overall goals of this course are given below. Focus is on the theoretical background and idealised concepts. Students have basic knowledge on atmospheric turbulence and theoretical as well as practical approaches to treat Planetary Boundary Layer flows. They are familiar with the relevant processes (turbulent transport, forcing) within, and typical states of the Planetary Boundary Layer. Idealized concepts are known as well as their adaptations under real surface conditions (as for example over complex topography). | |||||

Content | - Introduction - Turbulence - Statistical tratment of turbulence, turbulent transport - Conservation equations in a turbulent flow - Closure problem and closure assumptions - Scaling and similarity theory - Spectral characteristics - Concepts for non-ideal boundary layer conditions | |||||

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 | |||||

701-1221-00L | Dynamics of Large-Scale Atmospheric Flow | W | 4 credits | 2V + 1U | H. Wernli, S. Pfahl | |

Abstract | Dynamic, synoptic Meteorology | |||||

Objective | Understanding the dynamics of large-scale atmospheric flow | |||||

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 | |||||

401-5930-00L | Seminar in Physics of the Atmosphere for CSE | W | 4 credits | 2S | H. Joos, C. Schär | |

Abstract | The students of this course are provided with an introduction into presentation techniques (talks and posters) and practice this knowledge by making an oral presentation about a classical or recent scientific publication. | |||||

Objective | ||||||

Chemistry | ||||||

Number | Title | Type | ECTS | Hours | Lecturers | |

529-0004-00L | Computer Simulation in Chemistry, Biology and Physics | W | 7 credits | 4G | P. H. Hünenberger | |

Abstract | Molecular models, Force fields, Boundary conditions, Electrostatic interactions, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. For more information: www.csms.ethz.ch/education/CSCBP | |||||

Objective | Introduction to computer simulation of (bio)molecular systems, development of skills to carry out and interpret computer simulations of biomolecular systems. | |||||

Content | Molecular models, Force fields, Spatial boundary conditions, Calculation of Coulomb forces, Molecular dynamics, Analysis of trajectories, Quantum-mechanical simulation, Structure refinement, Application to real systems. Exercises: Analysis of papers on computer simulation, Molecular simulation in practice, Validation of molecular dynamics simulation. | |||||

Lecture notes | Available (copies of powerpoint slides distributed before each lecture) | |||||

Literature | See: www.csms.ethz.ch/education/CSCBP | |||||

Prerequisites / Notice | Since the exercises on the computer do convey and test essentially different skills as 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. For more information about the lecture: www.csms.ethz.ch/education/CSCBP | |||||

529-0003-00L | Advanced Quantum Chemistry | W | 7 credits | 3G | M. Reiher, S. Knecht | |

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 | |||||

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 or suggested by the supervisor in the area of computer simulation in chemistry, the results of which are to be presented both orally and in written form. For more information: www.csms.ethz.ch/education/RW | |||||

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. | ||||||

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. | |||||

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 | |||||

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 | |||||

151-0182-00L | Fundamentals of CFD Methods | W+ | 4 credits | 3G | A. Haselbacher | |

Abstract | This course is focused on providing students with the knowledge and understanding required to develop simple computational fluid dynamics (CFD) codes to solve the incompressible Navier-Stokes equations and to critically assess the results produced by CFD codes. As part of the course, students will write their own codes and verify and validate them systematically. | |||||

Objective | 1. Students know and understand basic numerical methods used in CFD in terms of accuracy and stability. 2. Students have a basic understanding of a typical simple CFD code. 3. Students understand how to assess the numerical and physical accuracy of CFD results. | |||||

Content | 1. Governing and model equations. Brief review of equations and properties 2. Overview of basic concepts: Overview of discretization process and its consequences 3. Overview of numerical methods: Finite-difference and finite-volume methods 4. Analysis of spatially discrete equations: Consistency, accuracy, stability, convergence of semi-discrete methods 5. Time-integration methods: LMS and RK methods, consistency, accuracy, stability, convergence 6. Analysis of fully discrete equations: Consistency, accuracy, stability, convergence of fully discrete methods 7. Solution of one-dimensional advection equation: Motivation for and consequences of upwinding, Godunov's theorem, TVD methods, DRP methods 8. Solution of two-dimensional advection equation: Dimension-by-dimension methods, dimensional splitting, multidimensional methods 9. Solution of one- and two-dimensional diffusion equations: Implicit methods, ADI methods 10. Solution of one-dimensional advection-diffusion equation: Numerical vs physical viscosity, boundary layers, non-uniform grids 11. Solution of incompressible Navier-Stokes equations: Incompressibility constraint and consequences, fractional-step and pressure-correction methods 12. Solution of incompressible Navier-Stokes equations on unstructured grids | |||||

Lecture notes | The course is based mostly on notes developed by the instructor. | |||||

Literature | Literature: There is no required textbook. Suggested references are: 1. H.K. Versteeg and W. Malalasekera, An Introduction to Computational Fluid Dynamics, 2nd ed., Pearson Prentice Hall, 2007 2. R.H. Pletcher, J.C. Tannehill, and D. Anderson, Computational Fluid Mechanics and Heat Transfer, 3rd ed., Taylor & Francis, 2011 | |||||

Prerequisites / Notice | Prior knowledge of fluid dynamics, applied mathematics, basic numerical methods, and programming in Fortran and/or C++ (knowledge of MATLAB is *not* sufficient). | |||||

151-0105-00L | Quantitative Flow Visualization | W | 4 credits | 2V + 1U | T. Rösgen | |

Abstract | The course provides an introduction to digital image analysis in modern flow diagnostics. Different techniques which are discussed include image velocimetry, laser induced fluorescence, liquid crystal thermography and interferometry. The physical foundations and measurement configurations are explained. Image analysis algorithms are presented in detail and programmed during the exercises. | |||||

Objective | Introduction to modern imaging techniques and post processing algorithms with special emphasis on flow analysis and visualization. Understanding of hardware and software requirements and solutions. Development of basic programming skills for (generic) imaging applications. | |||||

Content | Fundamentals of optics, flow visualization and electronic image acquisition. Frequently used mage processing techniques (filtering, correlation processing, FFTs, color space transforms). Image Velocimetry (tracking, pattern matching, Doppler imaging). Surface pressure and temperature measurements (fluorescent paints, liquid crystal imaging, infrared thermography). Laser induced fluorescence. (Digital) Schlieren techniques, phase contrast imaging, interferometry, phase unwrapping. Wall shear and heat transfer measurements. Pattern recognition and feature extraction, proper orthogonal decomposition. | |||||

Lecture notes | available | |||||

Prerequisites / Notice | Prerequisites: Fluiddynamics I, Numerical Mathematics, programming skills. Language: German on request. | |||||

151-0213-00L | Fluid Dynamics with the Lattice Boltzmann Method | 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. | |||||

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. | |||||

151-0207-00L | Theory and Modeling of Reactive Flows | W | 4 credits | 3G | C. E. Frouzakis, I. Mantzaras | |

Abstract | The course first reviews the governing equations and combustion chemistry, setting the ground for the analysis of homogeneous gas-phase mixtures, laminar diffusion and premixed flames. Catalytic combustion and its coupling with homogeneous combustion are dealt in detail, and turbulent combustion modeling approaches are presented. Available numerical codes will be used for modeling. | |||||

Objective | Theory of combustion with numerical applications | |||||

Content | The analysis of realistic reactive flow systems necessitates the use of detailed computer models that can be constructed starting from first principles i.e. thermodynamics, fluid mechanics, chemical kinetics, and heat and mass transport. In this course, the focus will be on combustion theory and modeling. The reacting flow governing equations and the combustion chemistry are firstly reviewed, setting the ground for the analysis of homogeneous gas-phase mixtures, laminar diffusion and premixed flames. Heterogeneous (catalytic) combustion, an area of increased importance in the last years, will be dealt in detail along with its coupling with homogeneous combustion. Finally, approaches for the modeling of turbulent combustion will be presented. Available numerical codes will be used to compute the above described phenomena. Familiarity with numerical methods for the solution of partial differential equations is expected. | |||||

Lecture notes | Handouts | |||||

Prerequisites / Notice | NEW course | |||||

401-5950-00L | Seminar in Fluid Dynamics for CSE | W | 4 credits | 2S | P. Jenny, T. Rösgen | |

Abstract | Enlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics | |||||

Objective | Enlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics | |||||

Prerequisites / Notice | Contact Prof. P. Jenny or Prof. T. Rösgen before the beginning of the semester | |||||

Systems and Control | ||||||

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. | |||||

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. | |||||

227-0045-00L | Signals and Systems I | W | 4 credits | 2V + 2U | H. Bölcskei | |

Abstract | Signal theory and systems theory (continuous-time and discrete-time): Signal analysis in the time and frequency domains, signal spaces, Hilbert spaces, generalized functions, linear time-invariant systems, sampling theorems, discrete-time signals and systems, digital filter structures, Discrete Fourier Transform (DFT), finite-dimensional signals and systems, Fast Fourier Transform (FFT). | |||||

Objective | Introduction to mathematical signal processing and system theory. | |||||

Content | Signal theory and systems theory (continuous-time and discrete-time): Signal analysis in the time and frequency domains, signal spaces, Hilbert spaces, generalized functions, linear time-invariant systems, sampling theorems, discrete-time signals and systems, digital filter structures, Discrete Fourier Transform (DFT), finite-dimensional signals and systems, Fast Fourier Transform (FFT). | |||||

Lecture notes | Lecture notes, problem set with solutions. | |||||

227-0225-00L | Linear System Theory | W | 6 credits | 5G | M. Kamgarpour | |

Abstract | The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, their use in control, filtering, and estimation and their applications to areas ranging from avionics to systems biology. | |||||

Objective | By the end of the class students should be comfortable with the fundamental results in linear system theory and the mathematical tools used to derive them. | |||||

Content | - Rings, fields and linear spaces, normed linear spaces and inner product spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete time, time varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, canonical forms, Kalman decomposition. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle. - Realization theory. | |||||

Lecture notes | F.M. Callier and C.A. Desoer, "Linear System Theory", Springer-Verlag, 1991. | |||||

Prerequisites / Notice | Prerequisites: Control Systems I (227-0103-00) or equivalent and sufficient mathematical maturity. | |||||

252-0535-00L | Machine Learning | W | 8 credits | 3V + 2U + 2A | 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. | |||||

Objective | Students will be familiarized with the most important 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. A machine learning project 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: - Bayesian theory of optimal decisions - Maximum likelihood and Bayesian parameter inference - Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM) - Ensemble methods: Bagging and Boosting - Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off - Non parametric density estimation: Parzen windows, nearest nieghbour - Dimension reduction: principal component analysis (PCA) and beyond | |||||

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 at least have followed one previous course offered by the Machine Learning Institute (e.g., CIL or LIS) or an equivalent course offered by another institution. |

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