文章基本信息

标题：Adaptive Sampling for Structure-Preserving Model Order Reduction of Port-Hamiltonian Systems ⁎
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作者：Paul Schwerdtner ; Matthias Voigt
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：19
页码：143-148
DOI：10.1016/j.ifacol.2021.11.069
语种：English
出版社：Elsevier
摘要：AbstractWe 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.
关键词：Keywordsmodel reductionH-infinity optimizationstructured systemsport-Hamiltonian systemsstructure-preserving methods