# Search result: Catalogue data in Spring Semester 2021

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

Core Courses In the ‘core courses’ subcategory, at least two course units must be successfully completed. | ||||||

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

401-3632-00L | Computational Statistics | W | 8 credits | 3V + 1U | M. Mächler | |

Abstract | We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R. | |||||

Learning objective | The student obtains an overview of modern statistical methods for data analysis, including their algorithmic aspects and theoretical properties. The methods are applied using the statistical programming language R. | |||||

Content | See the class website | |||||

Prerequisites / Notice | At least one semester of (basic) probability and statistics. Programming experience is helpful but not required. | |||||

263-0007-00L | Advanced Systems Lab Only for master students, otherwise a special permission by the study administration of D-INFK is required. | W | 8 credits | 3V + 2U + 2A | M. Püschel, C. Zhang | |

Abstract | This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning. | |||||

Learning objective | Software performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project. | |||||

Content | The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing. Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course. | |||||

Prerequisites / Notice | Solid knowledge of the C programming language and matrix algebra. | |||||

261-5110-00L | Optimization for Data Science | W | 10 credits | 3V + 2U + 4A | B. Gärtner, D. Steurer, N. He | |

Abstract | This course provides an in-depth theoretical treatment of optimization methods that are particularly relevant in data science. | |||||

Learning objective | Understanding the theoretical guarantees (and their limits) of relevant optimization methods used in data science. Learning general paradigms to deal with optimization problems arising in data science. | |||||

Content | This course provides an in-depth theoretical treatment of optimization methods that are particularly relevant in machine learning and data science. In the first part of the course, we will first give a brief introduction to convex optimization, with some basic motivating examples from machine learning. Then we will analyse classical and more recent first and second order methods for convex optimization: gradient descent, Nesterov's accelerated method, proximal and splitting algorithms, subgradient descent, stochastic gradient descent, variance-reduced methods, Newton's method, and Quasi-Newton methods. The emphasis will be on analysis techniques that occur repeatedly in convergence analyses for various classes of convex functions. We will also discuss some classical and recent theoretical results for nonconvex optimization. In the second part, we discuss convex programming relaxations as a powerful and versatile paradigm for designing efficient algorithms to solve computational problems arising in data science. We will learn about this paradigm and develop a unified perspective on it through the lens of the sum-of-squares semidefinite programming hierarchy. As applications, we are discussing non-negative matrix factorization, compressed sensing and sparse linear regression, matrix completion and phase retrieval, as well as robust estimation. | |||||

Prerequisites / Notice | As background, we require material taught in the course "252-0209-00L Algorithms, Probability, and Computing". It is not necessary that participants have actually taken the course, but they should be prepared to catch up if necessary. | |||||

Fields of Specialization | ||||||

Astrophysics | ||||||

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

402-0394-00L | Theoretical CosmologySpecial Students UZH must book the module AST513 directly at UZH. | W | 10 credits | 4V + 2U | L. M. Mayer, J. Yoo | |

Abstract | This is the second of a two course series which starts with "General Relativity" and continues in the spring with "Theoretical Astrophysics and Cosmology", where the focus will be on applying general relativity to cosmology as well as developing the modern theory of structure formation in a cold dark matter Universe. | |||||

Learning objective | Learning the fundamentals of modern physical cosmology. This entails understanding the physical principles behind the description of the homogeneous Universe on large scales in the first part of the course, and moving on to the inhomogeneous Universe model where perturbation theory is used to study the development of structure through gravitational instability in the second part of the course. Modern notions of dark matter and dark energy will also be introduced and discussed. | |||||

Content | The course will cover the following topics: - Homogeneous cosmology - Thermal history of the universe, recombination, baryogenesis and nucleosynthesis - Dark matter and Dark Energy - Inflation - Perturbation theory: Relativistic and Newtonian - Model of structure formation and initial conditions from Inflation - Cosmic microwave background anisotropies - Spherical collapse and galaxy formation - Large scale structure and cosmological probes | |||||

Lecture notes | In 2021, the lectures will be live-streamed online at ETH from the Room HPV G5 at the lecture hours. The recordings will be available at the ETH website. The detailed information will be provided by the course website and the SLACK channel. | |||||

Literature | Suggested textbooks: H.Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution S. Carroll: Space-Time and Geometry: An Introduction to General Relativity S. Dodelson: Modern Cosmology Secondary textbooks: S. Weinberg: Gravitation and Cosmology V. Mukhanov: Physical Foundations of Cosmology E. W. Kolb and M. S. Turner: The Early Universe N. Straumann: General relativity with applications to astrophysics A. Liddle and D. Lyth: Cosmological Inflation and Large Scale Structure | |||||

Prerequisites / Notice | Knowledge of General Relativity is recommended. | |||||

Physics of the Atmosphere | ||||||

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

701-1216-00L | Numerical Modelling of Weather and Climate | W | 4 credits | 3G | C. Schär, J. Vergara Temprado, M. Wild | |

Abstract | The course provides an introduction to weather and climate models. It discusses how these models are built addressing both the dynamical core and the physical parameterizations, and it provides an overview of how these models are used in numerical weather prediction and climate research. As a tutorial, students conduct a term project and build a simple atmospheric model using the language PYTHON. | |||||

Learning objective | At the end of this course, students understand how weather and climate models are formulated from the governing physical principles, and how they are used for climate and weather prediction purposes. | |||||

Content | The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials. | |||||

Lecture notes | Slides and lecture notes will be made available at Link | |||||

Literature | List of literature will be provided. | |||||

Prerequisites / Notice | Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming. Previous experience with PYTHON is useful but not required. | |||||

701-1232-00L | Radiation and Climate Change | W | 3 credits | 2G | M. Wild | |

Abstract | This lecture focuses on the prominent role of radiation in the energy balance of the Earth and in the context of past and future climate change. | |||||

Learning objective | The aim of this course is to develop a thorough understanding of the fundamental role of radiation in the context of Earth's energy balance and climate change. | |||||

Content | The course will cover the following topics: Basic radiation laws; sun-earth relations; the sun as driver of climate change (faint sun paradox, Milankovic ice age theory, solar cycles); radiative forcings in the atmosphere: aerosol, water vapour, clouds; radiation balance of the Earth (satellite and surface observations, modeling approaches); anthropogenic perturbation of the Earth radiation balance: greenhouse gases and enhanced greenhouse effect, air pollution and global dimming; radiation-induced feedbacks in the climate system (water vapour feedback, snow albedo feedback); climate model scenarios under various radiative forcings. | |||||

Lecture notes | Slides will be made available | |||||

Literature | As announced in the course | |||||

701-1228-00L | Cloud Dynamics: Hurricanes | W | 4 credits | 3G | U. Lohmann | |

Abstract | Hurricanes are among the most destructive elements in the atmosphere. This lecture will discuss the physical requirements for their formation, life cycle, damage potential and their relationship to global warming. It also distinguishes hurricanes from thunderstorms and tornadoes. | |||||

Learning objective | At the end of this course students will be able to distinguish the formation and life cycle mechanisms of tropical cyclones from those of extratropical thunderstorms/cyclones, project how tropical cyclones change in a warmer climate based on their physics and evaluate different tropical cyclone modification ideas. | |||||

Content | see course outline at: https://iac.ethz.ch/edu/courses/master/modules/cloud-dynamics | |||||

Lecture notes | Slides will be made available | |||||

Literature | A literature list can be found here: https://www.iac.ethz.ch/edu/courses/master/modules/cloud_dynamics | |||||

Prerequisites / Notice | At least one introductory lecture in Atmospheric Science or Instructor's consent. This lecture will build on some concepts of atmospheric dynamics and their governing equations. Thus, mathematical knowledge will be needed to use the equations to understand the material of the course. | |||||

701-1270-00L | High Performance Computing for Weather and Climate | W | 3 credits | 3G | O. Fuhrer | |

Abstract | State-of-the-art weather and climate simulations rely on large and complex software running on supercomputers. This course focuses on programming methods and tools for understanding, developing and optimizing the computational aspects of weather and climate models. Emphasis will be placed on the foundations of parallel computing, practical exercises and emerging trends such as using GPUs. | |||||

Learning objective | After attending this course, students will be able to: - Understand a broad variety of high performance computing concepts relevant for weather and climate simulations - Work with weather and climate simulation codes that run on large supercomputers | |||||

Content | HPC Overview: - Why does weather and climate require HPC? - Today's HPC: Beowulf-style clusters, massively parallel architectures, hybrid computing, accelerators - Scaling / Parallel efficiency - Algorithmic motifs in weather and climate Writing HPC code: - Data locality and single node efficiency - Shared memory parallelism with OpenMP - Distributed memory parallelism with MPI - GPU computing - High-level programming and domain-specific languages | |||||

Literature | - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press, 2011 - Computer Organization and Design, D.H. Patterson and J.L. Hennessy - Parallel Computing, A. Grama, A. Gupta, G. Karypis, V. Kumar (https://www-users.cs.umn.edu/~karypis/parbook/) - Parallel Programming in MPI and OpenMP, V. Eijkhout (http://pages.tacc.utexas.edu/~eijkhout/pcse/html/index.html) | |||||

Prerequisites / Notice | - fundamentals of numerical analysis and atmospheric modeling - basic experience in a programming language (C/C++, Fortran, Python, …) - experience using command line interfaces in *nix environments (e.g., Unix, Linux) | |||||

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

Abstract | In this seminar, the process of writing a scientific proposal is introduced. The essential elements of a proposal, including the peer review process, are outlined and class exercises train scientific writing skills. Knowledge exchange between class participants is promoted through the preparation of a master thesis proposal and evaluation of each other's work. | |||||

Learning objective | Scientific writing skills How to effectively write a scientific proposal | |||||

Content | In this seminar, the process of writing a scientific proposal will be introduced. The essential elements of a proposal, including the peer review process, will be outlined and class exercises will train scientific writing skills. Knowledge exchange between class participants is promoted through the preparation of a master thesis proposal and evaluation of each other's work. | |||||

Prerequisites / Notice | In this seminar it is mandatory to write a proposal about an upcoming MSc thesis or semester project. If no such project is planned, this Seminar cannot be taken. Please contact the lecturers on time of you plan to take this seminar. | |||||

Chemistry | ||||||

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

529-0474-00L | Quantum Chemistry | W | 6 credits | 3G | M. Reiher, T. Weymuth | |

Abstract | Introduction into the basic concepts of electronic structure theory and into numerical methods of quantum chemistry. Exercise classes are designed to deepen the theory; practical case studies using quantum chemical software to provide a 'hands-on' expertise in applying these methods. | |||||

Learning objective | Nowadays, chemical research can be carried out in silico, an intellectual achievement for which Pople and Kohn have been awarded the Nobel prize of the year 1998. This lecture shows how that has been accomplished. It works out the many-particle theory of many-electron systems (atoms and molecules) and discusses its implementation into computer programs. A complete picture of quantum chemistry shall be provided that will allow students to carry out such calculations on molecules (for accompanying experimental work in the wet lab or as a basis for further study of the theory). | |||||

Content | Basic concepts of many-particle quantum mechanics. Derivation of the many-electron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with Hartree-Fock theory for the electronic problem to Moeller-Plesset perturbation theory and configuration interaction and to coupled cluster and multi-configurational approaches. Density functional theory. Case studies using quantum mechanical software. | |||||

Lecture notes | Hand-outs in German will be provided for each lecture (they are supplemented by (computer) examples that continuously illustrate how the theory works). All information regarding this course, including links to the online streaming, will be available on this web page: https://reiher.ethz.ch/courses-and-seminars/exercises/QC_2021.html | |||||

Literature | Textbooks on Quantum Chemistry: F.L. Pilar, Elementary Quantum Chemistry, Dover Publications I.N. Levine, Quantum Chemistry, Prentice Hall Hartree-Fock in basis set representation: A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill Textbooks on Computational Chemistry: F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons | |||||

Prerequisites / Notice | Basic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required | |||||

227-0161-00L | Molecular and Materials Modelling | W | 4 credits | 2V + 2U | D. Passerone, C. Pignedoli | |

Abstract | The course introduces the basic techniques to interpret experiments with contemporary atomistic simulation, including force fields or ab initio based molecular dynamics and Monte Carlo. Structural and electronic properties will be simulated hands-on for realistic systems. The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples. | |||||

Learning objective | The ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment. | |||||

Content | -Classical force fields in molecular and condensed phase systems -Methods for finding stationary states in a potential energy surface -Monte Carlo techniques applied to nanoscience -Classical molecular dynamics: extracting quantities and relating to experimentally accessible properties -From molecular orbital theory to quantum chemistry: chemical reactions -Condensed phase systems: from periodicity to band structure -Larger scale systems and their electronic properties: density functional theory and its approximations -Advanced molecular dynamics: Correlation functions and extracting free energies -The use of Smooth Overlap of Atomic Positions (SOAP) descriptors in the evaluation of the (dis)similarity of crystalline, disordered and molecular compounds | |||||

Lecture notes | A script will be made available and complemented by literature references. | |||||

Literature | D. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002. M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990. C. J. Cramer, Essentials of Computational Chemistry. Theories and Models, Wiley 2004 G. L. Miessler, P. J. Fischer, and Donald A. Tarr, Inorganic Chemistry, Pearson 2014. K. Huang, Statistical Mechanics, Wiley, 1987. N. W. Ashcroft, N. D. Mermin, Solid State Physics, Saunders College 1976. E. Kaxiras, Atomic and Electronic Structure of Solids, Cambridge University Press 2010. | |||||

327-0613-00L | Computer Applications: Finite Elements in Solids and Structures The course will only take place if at least 7 students are enrolled. | W | 4 credits | 2V + 2U | A. Gusev | |

Abstract | To introduce the Finite Element Method to the students with a general interest in the topic | |||||

Learning objective | To introduce the Finite Element Method to the students with a general interest in the topic | |||||

Content | Introduction; Energy formulations; Displacement finite elements; Solutions to the finite element equations; Linear elements; Convergence, compatibility and completeness; Higher order elements; Beam and frame elements, Plate and shell elements; Dynamics and vibration; Generalization of the Finite Element concepts (Galerkin-weighted residual and variational approaches) | |||||

Lecture notes | Autographie | |||||

Literature | - Astley R.J. Finite Elements in Solids and Structures, Chapman & Hill, 1992 - Zienkiewicz O.C., Taylor R.L. The Finite Element Method, 5th ed., vol. 1, Butterworth-Heinemann, 2000 | |||||

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 | |||||

Learning objective | ||||||

Fluid Dynamics One of the course units 151-0208-00L Computational Methods for Flow, Heat and Mass Transfer Problems 151-0212-00L Advanced CFD Methods is compulsory. | ||||||

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

151-0208-00L | Computational Methods for Flow, Heat and Mass Transfer Problems | O | 4 credits | 4G | D. W. Meyer-Massetti | |

Abstract | Numerical methods for the solution of flow, heat & mass transfer problems are presented and illustrated by analytical & computer exercises. | |||||

Learning objective | Knowledge of and practical experience with discretization and solution methods for computational fluid dynamics and heat and mass transfer problems | |||||

Content | - Introduction with application examples, steps to a numerical solution - Classification of PDEs, application examples - Finite differences - Finite volumes - Method of weighted residuals, spectral methods, finite elements - Stability analysis, consistency, convergence - Numerical solution methods, linear solvers The learning materials are illustrated with practical examples. | |||||

Lecture notes | Slides to be completed during the lecture will be handed out. | |||||

Literature | References are provided during the lecture. Notes in close agreement with the lecture material are available (in German). | |||||

Prerequisites / Notice | Basic knowledge in fluid dynamics, thermodynamics and programming (lecture: "Models, Algorithms and Data: Introduction to Computing") | |||||

151-0212-00L | Advanced CFD Methods | W | 4 credits | 2V + 1U | P. Jenny | |

Abstract | Fundamental and advanced numerical methods used in commercial and open-source CFD codes will be explained. The main focus is on numerical methods for conservation laws with discontinuities, which is relevant for trans- and hypersonic gas dynamics problems, but also CFD of incompressible flows, Direct Simulation Monte Carlo and the Lattice Boltzmann method are explained. | |||||

Learning objective | Knowing what's behind a state-of-the-art CFD code is not only important for developers, but also for users in order to choose the right methods and to achieve meaningful and accurate numerical results. Acquiring this knowledge is the main goal of this course. Established numerical methods to solve the incompressible and compressible Navier-Stokes equations are explained, whereas the focus lies on finite volume methods for compressible flow simulations. In that context, first the main theory and then numerical schemes related to hyperbolic conservation laws are explained, whereas not only examples from fluid mechanics, but also simpler, yet illustrative ones are considered (e.g. Burgers and traffic flow equations). In addition, two less commonly used yet powerful approaches, i.e., the Direct Simulation Monte Carlo (DSMC) and Lattice Boltzmann methods, are introduced. For most exercises a C++ code will have to be modified and applied. | |||||

Content | - Finite-difference vs. finite-element vs. finite-volume methods - Basic approach to simulate incompressible flows - Brief introduction to turbulence modeling - Theory and numerical methods for compressible flow simulations - Direct Simulation Monte Carlo (DSMC) - Lattice Boltzmann method | |||||

Lecture notes | Part of the course is based on the referenced books. In addition, the participants receive a manuscript and the slides. | |||||

Literature | "Computational Fluid Dynamics" by H. K. Versteeg and W. Malalasekera. "Finite Volume Methods for Hyperbolic Problems" by R. J. Leveque. | |||||

Prerequisites / Notice | Basic knowledge in - fluid dynamics - numerical mathematics - programming (programming language is not important, but C++ is of advantage) | |||||

151-0170-00L | Computational Multiphase Thermal Fluid Dynamics | W | 4 credits | 2V + 1U | F. Coletti, A. Dehbi, Y. Sato | |

Abstract | The course deals with fundamentals of the application of Computational Fluid Dynamics to gas-liquid flows as well as particle laden gas flows including aerosols. The course will present the current state of art in the field. Challenging examples, mainly from the fluid-machinery and plant, are discussed in detail. | |||||

Learning objective | Fundamentals of 3D multiphase flows (Definitions, Averages, Flow regimes), mathematical models (two-fluid model, Euler-Euler and Euler-Lagrange techniques), modeling of dispersed bubble flows (inter-phase forces, population balance and multi-bubble size class models), turbulence modeling, stratified and free-surface flows (interface tracking techniques such as VOF, level-sets and variants, modeling of surface tension), particulate and aerosol flows, particle tracking, one and two way coupling, random walk techniques to couple particle tracking with turbulence models, numerical methods and tools, industrial applications. | |||||

151-0110-00L | Compressible Flows | W | 4 credits | 2V + 1U | T. Rösgen | |

Abstract | Topics: unsteady one-dimensional subsonic and supersonic flows, acoustics, sound propagation, supersonic flows with shocks and Prandtl-Meyer expansions, flow around slender bodies, shock tubes, reaction fronts (deflagration and detonation). Mathematical tools: method of characteristics and selected numerical methods. | |||||

Learning objective | Illustration of compressible flow phenomena and introduction to the corresponding mathematical description methods. | |||||

Content | The interaction of compressibility and inertia is responsible for wave generation in a fluid. The compressibility plays an important role for example in unsteady phenomena, such as oscillations in gas pipelines or exhaust pipes. Compressibility effects are also important in steady subsonic flows with high Mach numbers (M>0.3) and in supersonic flows (e.g. aeronautics, turbomachinery). The first part of the lecture deals with wave propagation phenomena in one-dimensional subsonic and supersonic flows. The discussion includes waves with small amplitudes in an acoustic approximation and waves with large amplitudes with possible shock formation. The second part deals with plane, steady supersonic flows. Slender bodies in a parallel flow are considered as small perturbations of the flow and can be treated by means of acoustic methods. The description of the two-dimensional supersonic flow around bodies with arbitrary shapes includes oblique shocks and Prandtl-Meyer expansions etc.. Various boundary conditions, which are imposed for example by walls or free-jet boundaries, and interactions, reflections etc. are taken into account. | |||||

Lecture notes | not available | |||||

Literature | a list of recommended textbooks is handed out at the beginning of the lecture. | |||||

Prerequisites / Notice | prerequisites: Fluiddynamics I and II | |||||

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 | |||||

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

Prerequisites / Notice | Contact Prof. P. Jenny or PD Dr. D. Meyer-Massetti before the beginning of the semester | |||||

Systems and Control | ||||||

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

227-0216-00L | Control Systems II | W | 6 credits | 4G | R. Smith | |

Abstract | Introduction to basic and advanced concepts of modern feedback control. | |||||

Learning objective | Introduction to basic and advanced concepts of modern feedback control. | |||||

Content | This course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues. | |||||

Lecture notes | The slides of the lecture are available to download. | |||||

Literature | Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005. | |||||

Prerequisites / Notice | Prerequisites: Control Systems or equivalent | |||||

227-0224-00L | Stochastic SystemsDoes not take place this semester. | W | 4 credits | 2V + 1U | to be announced | |

Abstract | Probability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering. | |||||

Learning objective | Stochastic dynamic systems. Optimal control and filtering of stochastic systems. Examples in technology and finance. | |||||

Content | - Stochastic processes - Stochastic calculus (Ito) - Stochastic differential equations - Discrete time stochastic difference equations - Stochastic processes AR, MA, ARMA, ARMAX, GARCH - Kalman filter - Stochastic optimal control - Applications in finance and engineering | |||||

Lecture notes | H. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts |

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