Search result: Catalogue data in Spring Semester 2021

Computational Science and Engineering Master Information
Core Courses
In the ‘core courses’ subcategory, at least two course units must be successfully completed.
NumberTitleTypeECTSHoursLecturers
401-3632-00LComputational StatisticsW8 credits3V + 1UM. Mächler
AbstractWe 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.
ObjectiveThe 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.
ContentSee the class website
Prerequisites / NoticeAt least one semester of (basic) probability and statistics.

Programming experience is helpful but not required.
263-0007-00LAdvanced Systems Lab Information Restricted registration - show details
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
W8 credits3V + 2U + 2AM. Püschel, C. Zhang
AbstractThis 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.
ObjectiveSoftware 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.
ContentThe 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 / NoticeSolid knowledge of the C programming language and matrix algebra.
261-5110-00LOptimization for Data Science Information W10 credits3V + 2U + 4AB. Gärtner, D. Steurer, N. He
AbstractThis course provides an in-depth theoretical treatment of optimization methods that are particularly relevant in data science.
ObjectiveUnderstanding 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.
ContentThis 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 / NoticeAs 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
NumberTitleTypeECTSHoursLecturers
402-0394-00LTheoretical Cosmology
Special Students UZH must book the module AST513 directly at UZH.
W10 credits4V + 2UL. M. Mayer, J. Yoo
AbstractThis 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.
ObjectiveLearning 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.
ContentThe 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 notesIn 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.
LiteratureSuggested 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 / NoticeKnowledge of General Relativity is recommended.
Physics of the Atmosphere
NumberTitleTypeECTSHoursLecturers
701-1216-00LNumerical Modelling of Weather and Climate Information W4 credits3GC. Schär, J. Vergara Temprado, M. Wild
AbstractThe 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.
ObjectiveAt 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.
ContentThe 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 notesSlides and lecture notes will be made available at
Link
LiteratureList of literature will be provided.
Prerequisites / NoticePrerequisites: 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.
701-1232-00LRadiation and Climate ChangeW3 credits2GM. Wild
AbstractThis 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.
ObjectiveThe 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.
ContentThe 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 notesSlides will be made available
LiteratureAs announced in the course
701-1228-00LCloud Dynamics: Hurricanes Information W4 credits3GU. Lohmann
AbstractHurricanes 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.
ObjectiveAt 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.
Contentsee course outline at: https://iac.ethz.ch/edu/courses/master/modules/cloud-dynamics
Lecture notesSlides will be made available
LiteratureA literature list can be found here: https://www.iac.ethz.ch/edu/courses/master/modules/cloud_dynamics
Prerequisites / NoticeAt 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.
701-1270-00LHigh Performance Computing for Weather and ClimateW3 credits3GO. Fuhrer
AbstractState-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.
ObjectiveAfter 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
ContentHPC 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)
401-5930-00LSeminar in Physics of the Atmosphere for CSEW4 credits2SH. Joos, C. Schär
AbstractIn 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.
ObjectiveScientific writing skills
How to effectively write a scientific proposal
ContentIn 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 / NoticeIn 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
NumberTitleTypeECTSHoursLecturers
529-0474-00LQuantum ChemistryW6 credits3GM. Reiher, T. Weymuth
AbstractIntroduction 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.
ObjectiveNowadays, 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).
ContentBasic 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 notesHand-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
LiteratureTextbooks 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 / NoticeBasic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required
227-0161-00LMolecular and Materials Modelling Information W4 credits2V + 2UD. Passerone, C. Pignedoli
AbstractThe 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.
ObjectiveThe 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 notesA script will be made available and complemented by literature references.
LiteratureD. 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.
327-0613-00LComputer Applications: Finite Elements in Solids and Structures Information
The course will only take place if at least 7 students are enrolled.
W4 credits2V + 2UA. Gusev
AbstractTo introduce the Finite Element Method to the students with a general interest in the topic
ObjectiveTo introduce the Finite Element Method to the students with a general interest in the topic
ContentIntroduction; 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 notesAutographie
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
401-5940-00LSeminar 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: www.csms.ethz.ch/education/RW
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.
NumberTitleTypeECTSHoursLecturers
151-0208-00LComputational Methods for Flow, Heat and Mass Transfer ProblemsO4 credits4GD. W. Meyer-Massetti
AbstractNumerical methods for the solution of flow, heat & mass transfer problems are presented and illustrated by analytical & computer exercises.
ObjectiveKnowledge 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 notesSlides to be completed during the lecture will be handed out.
LiteratureReferences are provided during the lecture. Notes in close agreement with the lecture material are available (in German).
Prerequisites / NoticeBasic knowledge in fluid dynamics, thermodynamics and programming (lecture: "Models, Algorithms and Data: Introduction to Computing")
151-0212-00LAdvanced CFD MethodsW4 credits2V + 1UP. Jenny
AbstractFundamental 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.
ObjectiveKnowing 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 notesPart 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 / NoticeBasic knowledge in
- fluid dynamics
- numerical mathematics
- programming (programming language is not important, but C++ is of advantage)
151-0170-00LComputational Multiphase Thermal Fluid DynamicsW4 credits2V + 1UF. Coletti, A. Dehbi, Y. Sato
AbstractThe 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.
ObjectiveFundamentals 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.
151-0110-00LCompressible FlowsW4 credits2V + 1UT. Rösgen
AbstractTopics: 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.
ObjectiveIllustration of compressible flow phenomena and introduction to the corresponding mathematical description methods.
ContentThe 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 notesnot available
Literaturea list of recommended textbooks is handed out at the beginning of the lecture.
Prerequisites / Noticeprerequisites: Fluiddynamics I and II
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 PD Dr. D. Meyer-Massetti before the beginning of the semester
Systems and Control
NumberTitleTypeECTSHoursLecturers
227-0216-00LControl Systems II Information W6 credits4GR. Smith
AbstractIntroduction to basic and advanced concepts of modern feedback control.
ObjectiveIntroduction to basic and advanced concepts of modern feedback control.
ContentThis 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 notesThe slides of the lecture are available to download.
LiteratureSkogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / NoticePrerequisites:
Control Systems or equivalent
227-0224-00LStochastic Systems
Does not take place this semester.
W4 credits2V + 1Uto be announced
AbstractProbability. Stochastic processes. Stochastic differential equations. Ito. Kalman filters. St Stochastic optimal control. Applications in financial engineering.
ObjectiveStochastic 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 notesH. P. Geering et al., Stochastic Systems, Measurement and Control Laboratory, 2007 and handouts
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