Scientific Computing Seminar

Date and Place: Thursdays and hybrid (live in 32-349/online via Zoom). For detailed dates see below!

Content

In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everybody interested in the topics is welcome.

List of Talks

Event Information:

  • Thu
    08
    Feb
    2024

    SC Seminar: Anuja Chakraborty

    11:45Hybrid (Room 32-349 and via Zoom)

    Anuja Chakraborty, Department of Computer Science, University of Kaiserslautern-Landau (RPTU)

    Title: On Compressed Sensing and Dynamic Mode Decomposition

    Abstract:

    This talk is focused on the discussion regarding the development and application of compressed
    sensing strategies for computing the dynamic mode decomposition (DMD) from heavily subsampled
    or compressed data. The review was mainly done based on the paper of Brunton’s COMPRESSED
    SENSING AND DYNAMIC MODE DECOMPOSITION [1]. The compressed sensing techniques developed
    in this work result in DMD eigenvalues that are equivalent to those obtained from full-state data.
    This is significant as it enables a consistent representation of system dynamics even with highly
    reduced data. Using l1-minimization or greedy algorithms, the study demonstrates the possibility of
    reconstructing full-state DMD eigenvectors from the compressed DMD eigenvalues. This
    reconstruction allows for the recovery of detailed information about the system’s modes. These
    results rely on a number of theoretical advances which is covered in this report. Also, effectiveness of
    the proposed compressed sensing architecture is illustrated through two model systems where the
    first example is on designing a spatial signal from a sparse vector of Fourier coefficients with a linear
    dynamical system driving the coefficients and the second example is on a double gyre flow field,
    which is a model for chaotic mixing in the ocean. So, the theoretical insights and practical
    demonstrations enhance the understanding and applicability of DMD methodologies in diverse
    scenarios.

    [1] Steven L. Brunton, Joshua L. Proctor, Jonathan H. Tu, J. Nathan Kutz COMPRESSED SENSING AND
    DYNAMIC MODE DECOMPOSITION, Journal of Computational Dynamics American Institute of
    Mathematical Sciences, Volume 2, Number 2, December 2015

    How to join online

    You can join online via Zoom, using the following link:
    https://uni-kl-de.zoom.us/j/63123116305?pwd=Yko3WU9ZblpGR3lGUkVTV1kzMCtUUT09