2022/06/02 by Paul Schwerdtner, Schwerdtner, Paul, Tim Moser +5
Biochemistry, Genetics and Molecular Biology · Engineering · Materials Science · #Dynamical Systems (math.DS) #FOS: Electrical engineering #FOS: Mathematics #Fuel Cells and Related Materials #Machine Learning in Materials Science #Optimization and Control (math.OC) #Protein Structure and Dynamics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2206.01608
openalex publication_date 2022/06/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We develop optimization-based structure-preserving model order reduction (MOR) methods for port-Hamiltonian (pH) descriptor systems of differentiation index one. Descriptor systems in pH form permit energy-based modeling and intuitive coupling of physical systems across different physical domains, scales, and accuracies. This makes pH models well-suited building-blocks for component-wise modeling of large system networks. In this context, it is often necessary to preserve the pH structure during MOR. We discuss current projection-based and structure-preserving MOR algorithms for pH systems and present a new optimization-based framework for that task. The benefits of our method include a simplified treatment of algebraic constraints and often a higher accuracy of the resulting reduced-order model, which is demonstrated by several numerical examples.