Truncated $\mathcal{H}_2$ Optimal Model Order Reduction for Bilinear Quadratic Output Systems

We develop an $\mathcal{H}_2$ model reduction framework for polynomial bilinear systems with quadratic outputs. We define the associated $\mathcal{H}_2$ norm and show its consistency with the established norm in the original (non-polynomial) BQO case. We derive first-order necessary conditions for $\mathcal{H}_2$ optimal model reduction of these polynomial systems and use them to construct an efficient iterative algorithm for computing reduced BQO models that approximately satisfy these conditions. Numerical results on two examples demonstrate the accuracy and effectiveness of the proposed reduction approach.

Authors

Institutions

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-27
DOI
https://doi.org/10.48550/arxiv.2610.37387
Primary Topic
Model Reduction and Neural Networks
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Truncated $\mathcal{H}_2$ Optimal Model Order Reduction for Bilinear Quadratic Output Systems

Heike Faßbender, Serkan Gugercin A, Till Peters
arXiv (Cornell University)
Model Reduction and Neural Networks
preprint

Truncated $\mathcal{H}_2$ Optimal Model Order Reduction for Bilinear Quadratic Output Systems

Heike Faßbender, Serkan Gugercin A, Till Peters
preprint en

Abstract

We develop an $\mathcal{H}_2$ model reduction framework for polynomial bilinear systems with quadratic outputs. We define the associated $\mathcal{H}_2$ norm and show its consistency with the established norm in the original (non-polynomial) BQO case. We derive first-order necessary conditions for $\mathcal{H}_2$ optimal model reduction of these polynomial systems and use them to construct an efficient iterative algorithm for computing reduced BQO models that approximately satisfy these conditions. Numerical results on two examples demonstrate the accuracy and effectiveness of the proposed reduction approach.

arXiv (Cornell University)
Virginia Tech (US), Technische Universität Braunschweig (DE)
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon