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. Copyright (C) 2021 The Authors.