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

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.

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.

This 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.

The 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

The 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.