Learning of Frequency Response Functions in Electrical Engineering

Niklas Georg (1, 2) , Julien Bect (3, 4) , Ulrich Römer (1) , Sebastian Schöps (2)

1 Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig]
2 Darmstadt University of Technology [Darmstadt]
3 L2S - Laboratoire des signaux et systèmes
4 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques

We present a new machine-learning approach for interpolation of frequency response functions, e.g. for electric circuits, based on a rational kernel-based interpolation method. The suggested method combines Szegö kernel interpolation with a suitable pseudo-kernel and a few rational basis functions inspired from vector fitting as well as a dedicated tuning and model selection procedure. We show that the method yields comparable accuracy as the established state-of-the-art methods adaptive Antoulas-Anderson and vector fitting for the considered benchmarks.

Domains: Engineering Sciences [physics]; Electromagnetism

Niklas Georg, Julien Bect, Ulrich Römer, Sebastian Schöps. Learning of Frequency Response Functions in Electrical Engineering. 14th International Conference on Scientific Computing in Electrical Engineering, SCEE 2022, SCEE Foundation, Jul 2022, Amsterdam, Netherlands. ⟨hal-04017621⟩