Model Reduction and Neural Networks
Open access
Peer reviewed
Abstract
Abstract
Abstract This work proposes a novel structure-preserving model reduction () method for linear, time-invariant port-Hamiltonian () systems. Our goal is to construct a reduced-order system, which can still be interpreted in the physical domain of the full order model. By this we mean, that if an electrical circuit is the initial high-dimensional system, we want the reduced-order model to be still interpretable as an electronic circuit. In the case of the well-known mass spring damper () system, there are methods available, which already guarantee the preservation of this particular structure. Moreover, we show that our new structure-preserving method, which is based on symplectic methods, will recover the known second-order Arnoldi method in the case of systems. However, for the example of an electrical circuit model (and more models of similar block structure), our method yields a novel model reduction method. We present numerical results on the aforementioned electronic circuit model, highlighting the advantages of the proposed method.
Direct answer
What can I do from this paper page?
Use this page to scan "Symplectic model order reduction of port-Hamiltonian systems" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Model Reduction and Neural Networks research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Mamunuzzaman2026Symplectic,
title = {Symplectic model order reduction of port-Hamiltonian systems},
author = {Mir Mamunuzzaman and Hans Zwart},
journal = {Mathematics of Control Signals and Systems},
year = {2026},
doi = {10.1007/s00498-026-00456-w},
url = {https://doi.org/10.1007/s00498-026-00456-w}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Model Reduction and Neural Networks research papers?
Follow Model Reduction and Neural Networks research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Symplectic model order reduction of port-Hamiltonian systems. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app