Search result: Catalogue data in Autumn Semester 2016

Computational Science and Engineering Master Information
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.
UZH Module Code: AST512

Mind the enrolment deadlines at UZH:
Link
W10 credits4V + 2UR. Teyssier
AbstractRadiative 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 / NoticePrerequisites: Elementary atomic physics, thermodynamics, mechanics, fluid dynamics.
Introduction to astrophysics (preferred but not obligatory).
401-7855-00LComputational 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:
Link
W6 credits2VL. M. Mayer
AbstractAcquire 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
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 Link as an example), some prior experience programming, knowledge of C, C++ beneficial
Physics of the Atmosphere
NumberTitleTypeECTSHoursLecturers
701-0023-00LAtmosphere Information W3 credits2VH. Wernli, E. 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.
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 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.
ObjectiveOverall 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 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
701-1221-00LDynamics of Large-Scale Atmospheric Flow Information W4 credits2V + 1UH. Wernli, S. Pfahl
AbstractDynamic, synoptic Meteorology
ObjectiveUnderstanding the dynamics of large-scale atmospheric flow
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
401-5930-00LSeminar in Physics of the Atmosphere for CSEW4 credits2SH. Joos, C. Schär
AbstractThe 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
NumberTitleTypeECTSHoursLecturers
529-0004-00LComputer Simulation in Chemistry, Biology and Physics Restricted registration - show details W7 credits4GP. H. Hünenberger
AbstractMolecular 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: Link
ObjectiveIntroduction to computer simulation of (bio)molecular systems, development of skills to carry out and interpret computer simulations of biomolecular systems.
ContentMolecular 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 notesAvailable (copies of powerpoint slides distributed before each lecture)
LiteratureSee: Link
Prerequisites / NoticeSince the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam.

For more information about the lecture: Link
529-0003-00LAdvanced Quantum ChemistryW7 credits3GM. Reiher, S. Knecht
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
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
Link
5) K. H. Marti, M. Reiher, New Electron Correlation Theories for Transition Metal Chemistry, Phys. Chem. Chem. Phys. 13 (2011) 6750
Link
6) K.H. Marti, M. Reiher, The Density Matrix Renormalization Group Algorithm in Quantum Chemistry, Z. Phys. Chem. 224 (2010) 583
Link
7) E. Mátyus, J. Hutter, U. Müller-Herold, M. Reiher, On the emergence of molecular structure, Phys. Rev. A 83 2011, 052512
Link

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 CSEW4 credits2SP. H. Hünenberger, M. Reiher
AbstractThe 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: Link
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.
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
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
151-0182-00LFundamentals of CFD MethodsW+4 credits3GA. Haselbacher
AbstractThis 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.
Objective1. 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.
Content1. 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 notesThe course is based mostly on notes developed by the instructor.
LiteratureLiterature: 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 / NoticePrior knowledge of fluid dynamics, applied mathematics, basic numerical methods, and programming in Fortran and/or C++ (knowledge of MATLAB is *not* sufficient).
151-0105-00LQuantitative Flow VisualizationW4 credits2V + 1UT. Rösgen
AbstractThe 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.
ObjectiveIntroduction 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.
ContentFundamentals 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 notesavailable
Prerequisites / NoticePrerequisites: Fluiddynamics I, Numerical Mathematics, programming skills.
Language: German on request.
151-0213-00LFluid Dynamics with the Lattice Boltzmann MethodW4 credits3GI. Karlin
AbstractThe course provides an introduction to theoretical foundations and practical usage of the Lattice Boltzmann Method for fluid dynamics simulations.
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.
151-0207-00LTheory and Modeling of Reactive FlowsW4 credits3GC. E. Frouzakis, I. Mantzaras
AbstractThe 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.
ObjectiveTheory of combustion with numerical applications
ContentThe 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 notesHandouts
Prerequisites / NoticeNEW course
401-5950-00LSeminar in Fluid Dynamics for CSE Restricted registration - show details W4 credits2SP. Jenny, T. Rösgen
AbstractEnlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics
ObjectiveEnlarged knowledge and practical abilities in fundamentals and applications of Computational Fluid Dynamics
Prerequisites / NoticeContact Prof. P. Jenny or Prof. T. Rösgen 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.
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.
227-0045-00LSignals and Systems IW4 credits2V + 2UH. Bölcskei
AbstractSignal 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).
ObjectiveIntroduction to mathematical signal processing and system theory.
ContentSignal 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 notesLecture notes, problem set with solutions.
227-0225-00LLinear System TheoryW6 credits5GM. Kamgarpour
AbstractThe 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.
ObjectiveBy 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 notesF.M. Callier and C.A. Desoer, "Linear System Theory", Springer-Verlag, 1991.
Prerequisites / NoticePrerequisites: Control Systems I (227-0103-00) or equivalent and sufficient mathematical maturity.
252-0535-00LMachine Learning Information W8 credits3V + 2U + 2AJ. 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.
ObjectiveStudents 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.
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:

- 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 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 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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