MATH-656 / 3 credits

Teacher(s): Drmac Zlatko, Kressner Daniel

Language: English

Remark: Spring semester


Frequency

Only this year

Summary

The Dynamic Mode Decomposition (DMD) has become a tool of trade in computational data driven analysis of complex dynamical systems. The DMD is deeply connected with the Koopman spectral analysis of nonlinear dynamical systems. This course will present recent results in this area.

Content

Note

The lectures will take place during 4 weeks (March  6 - April 11), with 4 hours of lectures/week.
The work on the (mini)projects will be carried out from end of March until end of April.

Keywords

dynamical systems, data driven analysis, model reduction, numerical linear algebra

Learning Prerequisites

Required courses

It is recommended that the participants have basic background in numerical analysis and dynamical systems. Programming skills (Matlab, Python or similar) are also required.

Learning Outcomes

By the end of the course, the student must be able to:

  • Identify and understand the state-of-the-art in data driven analysis
  • Apply DMD / Koopman operator analysis to complex dynamical systems

Resources

Bibliography

Lecture material and references will posted online in due time

Moodle Link

In the programs

  • Exam form: Project report (session free)
  • Subject examined: Numerical linear algebra for Koopman and DMD
  • Lecture: 16 Hour(s)
  • Project: 52 Hour(s)
  • Type: optional

Reference week

Related courses

Results from graphsearch.epfl.ch.