activity
20142026
most citedOperator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

131 citations · 917 across the 125 of their papers we have counts for

collaborators

139 papers

math.OC2026

Robust forms for passive descriptor systems

Peter Benner, Anshul Prajapati, Paul Van Dooren

We show how to compute the passivity radius of a given generalized state-space model of a rational transfer function that represents a passive system, both in the continuous-time a…

cs.LG2023

Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics

Pawan Goyal, Süleyman Yıldız, Peter Benner

Discovering a suitable coordinate transformation for nonlinear systems enables the construction of simpler models, facilitating prediction, control, and optimization for complex no…

math.OC2023★ 4 cited

Periodic optimal control of a plug flow reactor model with an isoperimetric constraint

Yevgeniia Yevgenieva, Alexander Zuyev, Peter Benner +1

We study a class of nonlinear hyperbolic partial differential equations with boundary control. This class describes chemical reactions of the type `` product'' carried out i…

math.NA2023

Parameterized Interpolation of Passive Systems

Peter Benner, Pawan Goyal, Paul Van Dooren

We study the tangential interpolation problem for a passive transfer function in standard state-space form. We derive new interpolation conditions based on the computation of a def…

math.NA2023★ 1 cited

Linearly Implicit Global Energy Preserving Reduced-order Models for Cubic Hamiltonian Systems

Süleyman Yildiz, Pawan Goyal, Peter Benner

This work discusses the model reduction problem for large-scale multi-symplectic PDEs with cubic invariants. For this, we present a linearly implicit global energy-preserving metho…

cs.LG2023★ 20 cited

Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference

Pawan Goyal, Igor Pontes Duff, Peter Benner

Scientific machine learning for inferring dynamical systems combines data-driven modeling, physics-based modeling, and empirical knowledge. It plays an essential role in engineerin…