Adaptive Sampling for Structure Preserving Model Order Reduction of Port-Hamiltonian Systems

2021/06/21 by Paul Schwerdtner, Schwerdtner, Paul, Matthias Voigt +1
Physics and Astronomy · Mathematics · Engineering · #Model Reduction and Neural Networks #Numerical methods for differential equations #Nuclear reactor physics and engineering

paper · pdf · doi:10.48550/arxiv.2106.11366

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.

Related

• Structure-Preserving Model Order Reduction for Index One Port-Hamiltonian Descriptor Systems
• SOBMOR: Structured Optimization-Based Model Order Reduction
• Structure-Preserving Model Reduction for Port-Hamiltonian Systems Based on a Special Class of Nonlinear Approximation Ansatzes
• Structure-Preserving Model Order Reduction for Index Two Port-Hamiltonian Descriptor Systems
• Structured Optimization-Based Model Order Reduction for Parametric Systems