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Archive / INF Seminars / INF_2024_01_23_Matthias_Voigt

USI - Email

	Structure-preserving model reduction of port-Hamiltonian descriptor systems by optimization

Host: Andrea Angino and Prof. Rolf Krause

Tuesday

23.01

USI Campus Est, Sector D, room D0.02

11:00 - 12:00


	Matthias Voigt Assistant Professor in Mathematics, Unidistance Brig

Abstract:
We present a new optimization-based structure-preserving model order reduction (MOR) method for port-Hamiltonian differential-algebraic equations (pH-DAEs). Our method is based on a novel parameterization that allows us to represent any linear time-invariant pH-DAE of a prescribed model order. We propose two algorithms which directly optimize the parameters of a reduced model to approximate a given large-scale model with respect to either the H∞ or the H2 norm. This approach has several benefits. Our parameterization ensures that the reduced model is again a pH-DAE system and enables a compact representation of the algebraic part of the large-scale model, which in projection-based methods often require a more involved treatment. The direct optimization is entirely based on transfer function evaluations of the large-scale model and is therefore independent of the structure of the system matrices. Numerical experiments are conducted to illustrate the high accuracy and small reduced model orders in comparison to other structure-preserving MOR methods.
This is joint work with Paul Schwerdtner (New York University), Tim Moser (TU Munich), and Volker Mehrmann (TU Berlin).