A non-intrusive data-based reformulation of a hybrid projection-based model reduction method
Numerical Analysis
2024-07-19 v1 Numerical Analysis
Dynamical Systems
Abstract
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.
Keywords
Cite
@article{arxiv.2407.13073,
title = {A non-intrusive data-based reformulation of a hybrid projection-based model reduction method},
author = {Ion Victor Gosea and Serkan Gugercin and Christopher Beattie},
journal= {arXiv preprint arXiv:2407.13073},
year = {2024}
}