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🆕 Date Issued:
2021-11-19
🗄 In DepositOnce:2023-05-10
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Adaptive Sampling for Structure-Preserving Model Order Reduction of Port-Hamiltonian Systems

Schwerdtner, Paul; Voigt, Matthias

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.
model reductionH-infinity optimizationstructured systemsport-Hamiltonian systemsstructure-preserving methods
Is Part Of
10.14279/depositonce-17164
Published in: IFAC-PapersOnLine, 10.1016/j.ifacol.2021.11.069, Elsevier
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