Search result: Catalogue data in Autumn Semester 2021

Computational Science and Engineering Bachelor Information
First Year Compulsory Courses
First Year Examination Block 1
NumberTitleTypeECTSHoursLecturers
401-0151-00LLinear Algebra Information Restricted registration - show details O5 credits3V + 2UV. C. Gradinaru
AbstractContents: Linear systems - the Gaussian algorithm, matrices - LU decomposition, determinants, vector spaces, least squares - QR decomposition, linear maps, eigenvalue problem, normal forms - singular value decomposition; numerical aspects; introduction to MATLAB.
ObjectiveEinführung in die Lineare Algebra für Ingenieure unter Berücksichtigung numerischer Aspekte
Lecture noteseigenes Aufschrieb und K. Nipp / D. Stoffer, Lineare Algebra, vdf Hochschulverlag, 5. Auflage 2002
LiteratureK. Nipp / D. Stoffer, Lineare Algebra, vdf Hochschulverlag, 5. Auflage 2002
Fostered competenciesFostered competencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Problem-solvingassessed
Social CompetenciesCommunicationnot assessed
Cooperation and Teamworknot assessed
Personal CompetenciesCreative Thinkingassessed
Critical Thinkingassessed
252-0025-01LDiscrete Mathematics Information O7 credits4V + 2UU. Maurer
AbstractContent: Mathematical reasoning and proofs, abstraction. Sets, relations (e.g. equivalence and order relations), functions, (un-)countability, number theory, algebra (groups, rings, fields, polynomials, subalgebras, morphisms), logic (propositional and predicate logic, proof calculi).
ObjectiveThe primary goals of this course are (1) to introduce the most important concepts of discrete mathematics, (2) to understand and appreciate the role of abstraction and mathematical proofs, and (3) to discuss a number of applications, e.g. in cryptography, coding theory, and algorithm theory.
ContentSee course description.
Lecture notesavailable (in english)
252-0856-00LComputer Science Information O4 credits2V + 2UF. Friedrich Wicker, R. Sasse
AbstractThe course covers the fundamental concepts of computer programming with a focus on systematic algorithmic problem solving. Taught language is C++. No programming experience is required.
ObjectivePrimary educational objective is to learn programming with C++. After having successfully attended the course, students have a good command of the mechanisms to construct a program. They know the fundamental control and data structures and understand how an algorithmic problem is mapped to a computer program. They have an idea of what happens "behind the scenes" when a program is translated and executed. Secondary goals are an algorithmic computational thinking, understanding the possibilities and limits of programming and to impart the way of thinking like a computer scientist.
ContentThe course covers fundamental data types, expressions and statements, (limits of) computer arithmetic, control statements, functions, arrays, structural types and pointers. The part on object orientation deals with classes, inheritance and polymorphism; simple dynamic data types are introduced as examples. In general, the concepts provided in the course are motivated and illustrated with algorithms and applications.
Lecture notesEnglish lecture notes will be provided during the semester. The lecture notes and the lecture slides will be made available for download on the course web page. Exercises are solved and submitted online.
LiteratureBjarne Stroustrup: Einführung in die Programmierung mit C++, Pearson Studium, 2010
Stephen Prata, C++ Primer Plus, Sixth Edition, Addison Wesley, 2012
Andrew Koenig and Barbara E. Moo: Accelerated C++, Addison-Wesley, 2000
First Year Examination Block 2
NumberTitleTypeECTSHoursLecturers
401-0231-10LAnalysis 1 Information Restricted registration - show details
Students in BSc EEIT may instead register for 401-1261-07L Analysis I: One Variable (for BSc Mathematics, BSc Physics and BSc Interdisciplinary Science (Phys Chem)) and take the performance assessment of the corresponding two-semester course. Students in BSc EEIT who wish to register for 401-1261-07L/401-1262-07L Analysis I: One Variable/Analysis II: Several Variables instead of 401-0231-10L/401-0232-10L Analysis 1/Analysis 2 must get in touch with the Study Administration before the registration.
O8 credits4V + 3UT. Rivière
AbstractReelle und komplexe Zahlen, Grenzwerte, Folgen, Reihen, Potenzreihen, stetige Abbildungen, Differential- und Integralrechnung einer Variablen, Einführung in gewöhnliche Differentialgleichungen
ObjectiveEinführung in die Grundlagen der Analysis
Lecture notesChristian Blatter: Ingenieur-Analysis (Kapitel 1-4)
LiteratureKonrad Koenigsberger, Analysis I.
Christian Blatter, Analysis I.
402-0043-00LPhysics IO4 credits3V + 1UJ. Home
AbstractIntroduction to the concepts and tools in physics with the help of demonstration experiments: mechanics of point-like and ridged bodies, periodic motion and mechanical waves.
ObjectiveThe concepts and tools in physics, as well as the methods of an experimental science are taught. The student should learn to identify, communicate and solve physical problems in his/her own field of science.
ContentMechanics (motion, Newton's laws, work and energy, conservation of momentum, rotation, gravitation, fluids)
Periodic Motion and Waves (periodic motion, mechanical waves, acoustics).
Lecture notesThe lecture follows the book "Physics" by Paul A. Tipler.
LiteraturePaul A. Tipler and Gene P. Mosca, Physics (for Scientists and Engineers), W. H. Freeman and Company
Basic Courses
Block G1
NumberTitleTypeECTSHoursLecturers
401-0353-00LAnalysis 3 Information Restricted registration - show details O4 credits2V + 2UM. Iacobelli
AbstractIn this lecture we treat problems in applied analysis. The focus lies on the solution of quasilinear first order PDEs with the method of characteristics, and on the study of three fundamental types of partial differential equations of second order: the Laplace equation, the heat equation, and the wave equation.
ObjectiveThe aim of this class is to provide students with a general overview of first and second order PDEs, and teach them how to solve some of these equations using characteristics and/or separation of variables.
Content1.) General introduction to PDEs and their classification (linear, quasilinear, semilinear, nonlinear / elliptic, parabolic, hyperbolic)

2.) Quasilinear first order PDEs
- Solution with the method of characteristics
- COnservation laws

3.) Hyperbolic PDEs
- wave equation
- d'Alembert formula in (1+1)-dimensions
- method of separation of variables

4.) Parabolic PDEs
- heat equation
- maximum principle
- method of separation of variables

5.) Elliptic PDEs
- Laplace equation
- maximum principle
- method of separation of variables
- variational method
LiteratureY. Pinchover, J. Rubinstein, "An Introduction to Partial Differential Equations", Cambridge University Press (12. Mai 2005)
Prerequisites / NoticePrerequisites: Analysis I and II, Fourier series (Complex Analysis)
401-0647-00LIntroduction to Mathematical OptimizationO5 credits2V + 1UD. Adjiashvili
AbstractIntroduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.
ObjectiveThe goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.
ContentTopics covered in this course include:
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...).
- Modelling with mathematical optimization: applications of mathematical programming in engineering.
LiteratureInformation about relevant literature will be given in the lecture.
Prerequisites / NoticeThis course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications.
401-2673-00LNumerical Methods for CSEO9 credits2V + 2U + 4PR. Hiptmair
AbstractThe course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++.
Objective* Knowledge of the fundamental algorithms in numerical mathematics
* Knowledge of the essential terms in numerical mathematics and the
techniques used for the analysis of numerical algorithms
* Ability to choose the appropriate numerical method for concrete problems
* Ability to interpret numerical results
* Ability to implement numerical algorithms afficiently
Content* Computing with Matrices and Vectors
* Direct Methods for linear systems of equations
* Least Squares Techniques
* Data Interpolation and Fitting
* Iterative Methods for non-linear systems of equations
* Filtering Algorithms
* Approximation of Functions
* Numerical Quadrature
Lecture notesLecture materials (PDF documents and codes) will be made available to the participants through the course web page, whose address will be announced in the beginning of the course.
LiteratureU. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011.

A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000.

W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006.

W. Gander, M.J. Gander, and F. Kwok "Scientific Computing", Springer 2014.

M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002

P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002
Prerequisites / NoticeThe course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Knowledge of C++ is taken for granted.
Fostered competenciesFostered competencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Media and Digital Technologiesnot assessed
Problem-solvingassessed
Project Managementnot assessed
Block G2
NumberTitleTypeECTSHoursLecturers
402-0811-00LProgramming Techniques for Scientific Simulations IO5 credits4GR. Käppeli
AbstractThis lecture provides an overview of programming techniques for scientific simulations. The focus is on basic and advanced C++ programming techniques and scientific software libraries. Based on an overview over the hardware components of PCs and supercomputer, optimization methods for scientific simulation codes are explained.
ObjectiveThe goal of the course is that students learn basic and advanced programming techniques and scientific software libraries as used and applied for scientific simulations.
252-0061-00LSystems Programming and Computer Architecture Information O7 credits4V + 2UT. Roscoe, A. Klimovic
AbstractIntroduction to systems programming. C and assembly language,
floating point arithmetic, basic translation of C into assembler,
compiler optimizations, manual optimizations. How hardware features
like superscalar architecture, exceptions and interrupts, caches,
virtual memory, multicore processors, devices, and memory systems
function and affect correctness, performance, and optimization.
ObjectiveThe course objectives are for students to:

1. Develop a deep understanding of, and intuition about, the execution
of all the layers (compiler, runtime, OS, etc.) between programs in
high-level languages and the underlying hardware: the impact of
compiler decisions, the role of the operating system, the effects
of hardware on code performance and scalability, etc.

2. Be able to write correct, efficient programs on modern hardware,
not only in C but high-level languages as well.

3. Understand Systems Programming as a complement to other disciplines
within Computer Science and other forms of software development.

This course does not cover how to design or build a processor or
computer.
ContentThis course provides an overview of "computers" as a
platform for the execution of (compiled) computer programs. This
course provides a programmer's view of how computer systems execute
programs, store information, and communicate. The course introduces
the major computer architecture structures that have direct influence
on the execution of programs (processors with registers, caches, other
levels of the memory hierarchy, supervisor/kernel mode, and I/O
structures) and covers implementation and representation issues only
to the extend that they are necessary to understand the structure and
operation of a computer system.

The course attempts to expose students to the practical issues that
affect performance, portability, security, robustness, and
extensibility. This course provides a foundation for subsequent
courses on operating systems, networks, compilers and many other
courses that require an understanding of the system-level
issues. Topics covered include: machine-level code and its generation
by optimizing compilers, address translation, input and output,
trap/event handlers, performance evaluation and optimization (with a
focus on the practical aspects of data collection and analysis).
Lecture notes- C programmnig
- Integers
- Pointers and dynamic memory allocation
- Basic computer architecture
- Compiling C control flow and data structures
- Code vulnerabilities
- Implementing memory allocation
- Linking
- Floating point
- Optimizing compilers
- Architecture and optimization
- Caches
- Exceptions
- Virtual memory
- Multicore
- Devices
LiteratureThe course is based in part on "Computer Systems: A Programmer's Perspective" (3rd Edition) by R. Bryant and D. O'Hallaron, with additional material.
Prerequisites / Notice252-0029-00L Parallel Programming
252-0028-00L Design of Digital Circuits
Block G3
All course units within Block G3 are offered in the spring semester.
Block G4
All course units within Block G4 are offered in the spring semester.
Core Courses from Group I (Modules)
Module A
NumberTitleTypeECTSHoursLecturers
151-0107-20LHigh Performance Computing for Science and Engineering (HPCSE) IW4 credits4GP. Koumoutsakos, S. M. Martin
AbstractThis course gives an introduction into algorithms and numerical methods for parallel computing on shared and distributed memory architectures. The algorithms and methods are supported with problems that appear frequently in science and engineering.
ObjectiveWith manufacturing processes reaching its limits in terms of transistor density on today’s computing architectures, efficient utilization of computing resources must include parallel execution to maintain scaling. The use of computers in academia, industry and society is a fundamental tool for problem solving today while the “think parallel” mind-set of developers is still lagging behind.

The aim of the course is to introduce the student to the fundamentals of parallel programming using shared and distributed memory programming models. The goal is on learning to apply these techniques with the help of examples frequently found in science and engineering and to deploy them on large scale high performance computing (HPC) architectures.
Content1. Hardware and Architecture: Moore’s Law, Instruction set architectures (MIPS, RISC, CISC), Instruction pipelines, Caches, Flynn’s taxonomy, Vector instructions (for Intel x86)

2. Shared memory parallelism: Threads, Memory models, Cache coherency, Mutual exclusion, Uniform and Non-Uniform memory access, Open Multi-Processing (OpenMP)

3. Distributed memory parallelism: Message Passing Interface (MPI), Point-to-Point and collective communication, Blocking and non-blocking methods, Parallel file I/O, Hybrid programming models

4. Performance and parallel efficiency analysis: Performance analysis of algorithms, Roofline model, Amdahl’s Law, Strong and weak scaling analysis

5. Applications: HPC Math libraries, Linear Algebra and matrix/vector operations, Singular value decomposition, Neural Networks and linear autoencoders, Solving partial differential equations (PDEs) using grid-based and particle methods
Lecture noteshttps://www.cse-lab.ethz.ch/teaching/hpcse-i_hs21/
Class notes, handouts
Literature• An Introduction to Parallel Programming, P. Pacheco, Morgan Kaufmann
• Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press
• Computer Organization and Design, D.H. Patterson and J.L. Hennessy, Morgan Kaufmann
• Vortex Methods, G.H. Cottet and P. Koumoutsakos, Cambridge University Press
• Lecture notes
Prerequisites / NoticeStudents should be familiar with a compiled programming language (C, C++ or Fortran). Exercises and exams will be designed using C++. The course will not teach basics of programming. Some familiarity using the command line is assumed. Students should also have a basic understanding of diffusion and advection processes, as well as their underlying partial differential equations.
Module B
NumberTitleTypeECTSHoursLecturers
263-2800-00LDesign of Parallel and High-Performance Computing Information Restricted registration - show details
Number of participants limited to 125.
W9 credits3V + 2U + 3AT. Hoefler, M. Püschel
AbstractAdvanced topics in parallel and high-performance computing.
ObjectiveUnderstand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large parallel high-performance software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore.
ContentWe will cover all aspects of high-performance computing ranging from architecture through programming up to algorithms. We will start with a discussion of caches and cache coherence in practical computer systems. We will dive into parallel programming concepts such as memory models, locks, and lock-free. We will cover performance modeling and parallel design principles as well as basic parallel algorithms.
Prerequisites / NoticeThis class is intended for the Computer Science Masters curriculum. Students must have basic knowledge in programming in C as well as computer science theory. Students should be familiar with the material covered in the ETH computer science first-year courses "Parallele Programmierung (parallel programming)" and "Algorithmen und Datenstrukturen (algorithm and data structures)" or equivalent courses.
Core Courses from Group II
No offering in the Autumn Semester.
Fields of Specialization
Astrophysics
NumberTitleTypeECTSHoursLecturers
401-7851-00LTheoretical Astrophysics (University of Zurich) Information
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: AST512

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

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

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

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html
W6 credits2VL. M. Mayer
Abstract
ObjectiveAcquire knowledge of main methodologies for computer-based models of astrophysical systems,the physical equations behind them, and train such knowledge with simple examples of computer programmes
Content1. Integration of ODE, Hamiltonians and Symplectic integration techniques, time adaptivity, time reversibility
2. Large-N gravity calculation, collisionless N-body systems and their simulation
3. Fast Fourier Transform and spectral methods in general
4. Eulerian Hydrodynamics: Upwinding, Riemann solvers, Limiters
5. Lagrangian Hydrodynamics: The SPH method
6. Resolution and instabilities in Hydrodynamics
7. Initial Conditions: Cosmological Simulations and Astrophysical Disks
8. Physical Approximations and Methods for Radiative Transfer in Astrophysics
LiteratureGalactic Dynamics (Binney & Tremaine, Princeton University Press),
Computer Simulation using Particles (Hockney & Eastwood CRC press),
Targeted journal reviews on computational methods for astrophysical fluids (SPH, AMR, moving mesh)
Prerequisites / NoticeSome knowledge of UNIX, scripting languages (see www.physik.uzh.ch/lectures/informatik/python/ as an example), some prior experience programming, knowledge of C, C++ beneficial
Physics of the Atmosphere
NumberTitleTypeECTSHoursLecturers
701-0023-00LAtmosphere Information W3 credits2VE. Fischer, T. Peter
AbstractBasic principles of the atmosphere, physical structure and chemical composition, trace gases, atmospheric cycles, circulation, stability, radiation, condensation, clouds, oxidation capacity and ozone layer.
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.
Chemistry
NumberTitleTypeECTSHoursLecturers
529-0004-01LClassical Simulation of (Bio)Molecular Systems Information W6 credits4GP. H. Hünenberger, J. Dolenc, S. Riniker
AbstractMolecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS).
ObjectiveIntroduction to classical (atomistic) computer simulation of (bio)molecular systems, development of skills to carry out and interpret these simulations.
ContentMolecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS).
Lecture notesThe powerpoint slides of the lectures will be made available weekly on the website in pdf format (on the day preceding each lecture).
LiteratureSee: www.csms.ethz.ch/education/CSBMS
Prerequisites / NoticeSince the exercises on the computer do convey and test essentially different skills than those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam (learning component, possible bonus of up to 0.25 points on the exam mark).

For more information about the lecture: www.csms.ethz.ch/education/CSBMS
Fluid Dynamics
NumberTitleTypeECTSHoursLecturers
151-0103-00LFluid Dynamics IIW3 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
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.
Robotics
Only one of the two course units
263-5902-00L Computer Vision resp.
227-0447-00L Image Analysis and Computer Vision
may be recognised for credits. More precisely, it is also not allowed to have recognised one course unit for the Bachelor's and the other course unit for the Master's degree.
The same restriction applied to the two course units
263-5210-00L Probabilistic Artificial Intelligence resp.
252-0535-00L Advanced Machine Learning
NumberTitleTypeECTSHoursLecturers
151-0601-00LTheory of Robotics and Mechatronics Information W4 credits3GP. Korba, S. Stoeter
AbstractThis course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
ObjectiveRobotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
ContentAn introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control.
Lecture notesavailable.
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