Adaptive Sampling for Structure-Preserving Model Order Reduction of Port-Hamiltonian Systems
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.
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Published in: IFAC-PapersOnLine, 10.1016/j.ifacol.2021.11.069, Elsevier