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

Mechthild, the HPC system of the institute, was extended, to fulfill the requirements of upcoming AI applications, this May. Five new compute nodes equipped with AMD EPYC 7452 CPUs and NVidia Tesla A100 accelerators were installed. Together with the existing GPU nodes, we now provide up to 22 accelerator devices for all kinds of applications ranging from numerical linear algebra and molecular dynamics to the latest AI toolkits. Alongside increasing the computational power of the system, the central storage was doubled to cope with the increased data demands.

This paper by P. Benner (CSC), S. Gugercin (VT) and S. W. R. Werner (CSC) provides a framework for structure-preserving interpolation of regular subsystem transfer functions of structured bilinear control systems. It got published in Advances in Computational Mathematics.

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 P. Benner (CSC) and S. W. R. Werner (CSC) extends the frequency- and time-limited balanced truncation methods to second-order systems and describes numerical tools for their application to large-scale sparse systems. It got published in Linear Algebra and its Applications.