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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References