Schwerdtner, Paul Voigt, Matthias 2021 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. application/pdf https://n2t.net/ark:/51647/srd1320163 https://sonar.unidistance.ch/global/documents/320163 https://sonar.unidistance.ch/documents/320163/files/SchV21.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ifacol.2021.11.069 info:eu-repo/semantics/altIdentifier/ark/51647/srd1320163 info:eu-repo/semantics/openAccess CC BY-NC-ND IFAC-PapersOnline. - 2021, vol. 54, no. 19, p. 143-148 model reduction H-infinity optimization structured systems port-Hamiltonian systems structure-preserving methods open access info:eu-repo/classification/udc/6 Adaptive sampling for structure-preserving model order reduction of port-Hamiltonian systems http://purl.org/coar/resource_type/c_6501