Adaptive Sampling for Structure-Preserving Model Order Reduction of Port-Hamiltonian Systems
Authors
Paul Schwerdtner, Matthias Voigt
Abstract
We present an adaptive sampling strategy for the optimization-based structure-preserving model order reduction (MOR) algorithm developed in [Schwerdtner, P. and Voigt, M. (2020). Structure-preserving model order reduction by parameter optimization, Preprint arXiv:2011.07567]. This strategy reduces the computational demand and the required a priori knowledge about the given full-order model, while at the same time retaining a high accuracy compared to other structure-preserving but also unstructured MOR algorithms. A numerical study with a port-Hamiltonian benchmark system demonstrates the effectiveness of our method when combined with this new adaptive sampling strategy. We also investigate the distribution of the sample points.
Keywords
model reduction; H-infinity optimization; structured systems; port-Hamiltonian systems; structure-preserving methods
Citation
- Journal: IFAC-PapersOnLine
- Year: 2021
- Volume: 54
- Issue: 19
- Pages: 143–148
- Publisher: Elsevier BV
- DOI: 10.1016/j.ifacol.2021.11.069
- Note: 7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control LHMNC 2021- Berlin, Germany, 11-13 October 2021
BibTeX
@article{Schwerdtner_2021,
title={{Adaptive Sampling for Structure-Preserving Model Order Reduction of Port-Hamiltonian Systems}},
volume={54},
ISSN={2405-8963},
DOI={10.1016/j.ifacol.2021.11.069},
number={19},
journal={IFAC-PapersOnLine},
publisher={Elsevier BV},
author={Schwerdtner, Paul and Voigt, Matthias},
year={2021},
pages={143--148}
}
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