Adaptive Sampling for Structure Preserving Model Order Reduction of Port-Hamiltonian Systems
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 combined with its new adaptive sampling strategy. We also investigate the distribution of the sample points.
Cite
@article{arxiv.2106.11366,
title = {Adaptive Sampling for Structure Preserving Model Order Reduction of Port-Hamiltonian Systems},
author = {Paul Schwerdtner and Matthias Voigt},
journal= {arXiv preprint arXiv:2106.11366},
year = {2021}
}
Comments
6 pages, 4 figures