A non-intrusive data-based reformulation of a hybrid projection-based model reduction method

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Hauptverfasser: Gosea, Ion Victor, Gugercin, Serkan, Beattie, Christopher
Format: Preprint
Veröffentlicht: 2024
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author Gosea, Ion Victor
Gugercin, Serkan
Beattie, Christopher
author_facet Gosea, Ion Victor
Gugercin, Serkan
Beattie, Christopher
contents We present a novel data-driven reformulation of the iterative SVD-rational Krylov algorithm (ISRK), in its original formulation a Petrov-Galerkin (two-sided) projection-based iterative method for model reduction combining rational Krylov subspaces (on one side) with Gramian/SVD based subspaces (on the other side). We show that in each step of ISRK, we do not necessarily require access to the original system matrices, but only to input/output data in the form of the system's transfer function, evaluated at particular values (frequencies). Numerical examples illustrate the efficiency of the new data-driven formulation.
format Preprint
id arxiv_https___arxiv_org_abs_2407_13073
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A non-intrusive data-based reformulation of a hybrid projection-based model reduction method
Gosea, Ion Victor
Gugercin, Serkan
Beattie, Christopher
Numerical Analysis
Dynamical Systems
We present a novel data-driven reformulation of the iterative SVD-rational Krylov algorithm (ISRK), in its original formulation a Petrov-Galerkin (two-sided) projection-based iterative method for model reduction combining rational Krylov subspaces (on one side) with Gramian/SVD based subspaces (on the other side). We show that in each step of ISRK, we do not necessarily require access to the original system matrices, but only to input/output data in the form of the system's transfer function, evaluated at particular values (frequencies). Numerical examples illustrate the efficiency of the new data-driven formulation.
title A non-intrusive data-based reformulation of a hybrid projection-based model reduction method
topic Numerical Analysis
Dynamical Systems
url https://arxiv.org/abs/2407.13073