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Computational Methods in Systems and Control Theory

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Research Group Computational Methods in Systems and Control Theory
(Prof. Dr. Peter Benner)

News

Preprint: Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach


May 11, 2021
This work by Pawan Goyal (CSC) and Peter Benner (CSC) combines machine learning (dictionary-based) with a numerical integration scheme, namely a Runge-Kutta scheme to discover governing equations using corrupted and sparsely-sampled data. The method does not require the computation of derivative information to discover governing equations. Hence, it holds a key advantage when data are corrupted and sparsely sampled.

Paper: Learning Reduced order Dynamics for Parametrized Shallow Water Equations from Data


May 10, 2021
This paper by Süleyman Yildiz (METU Ankara), Pawan Goyal (CSC), Peter Benner (CSC), and Bülent Karasözen (METU Ankara) discusses learning reduced models for parameterized shallow water equations using only data. The article is published in International Journal for Numerical Methods in Fluids.

Paper: Sustainable Research Software Hand-Over


April 30, 2021
handoverThis paper by J. Fehr (Uni Stuttgart), C. Himpe (CSC), S. Rave (WWU Münster) and J. Saak (CSC) intends to be a bottom-up approach for sustainable hand-over processes in scientific software development from a developer’s perspective. It got published in the Journal of Open Research Software today.

Software: M-M.E.S.S.-2.1: New feature release


April 30, 2021
mmessThe M.E.S.S. development team releases version 2.1 of the MATLAB and OCTAVE toolbox. The new version contains further improvements to the MOR functions, as well as various minor bug fixes. Most importantly, this version provides new solvers for Lyapunov-plus-positive equations and support for the new sparss and mechss system classes in Matlab

Events: BiGmax Workshop on Big-Data-Driven Materials Science


April 14, 2021
BiGmaxLogoThe BiGmax Workshop 2021 on Big-Data-Driven Materials Science will be held virtually from April 14 - 15, 2021. The workshop is aimed at presenting results and new insights into data-driven materials science. Those can be based on approaches in statistical and machine learning, compressed sensing and other recent technologies from mathematics, computer science, statistics and information technology.
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Upcoming seminar talks

Date Speaker(s) Title
18.05.21 Christian Himpe & Petar Mlinarić & Jens Saak & Steffen Werner MOR Software (co-developed in the CSC group)
08.06.21 Alessandro Alla (PUC-Rio) TBA


Date Speaker(s) Title
14.07.21 Team (MOR) A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Sridhar)
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems (Harshit)
29.09.21 Team (tba) (tba)


©2021, Max Planck Society, Munich
Jens Saak, saak@mpi-magdeburg.mpg.de
13 Mai 2021