7 citations · 28 across the 20 of their papers we have counts for
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Fast and Reliable Reduced-Order Models for Cardiac Electrophysiology
Sridhar Chellappa, Barış Cansız, Lihong Feng +2
Mathematical models of the human heart are increasingly playing a vital role in understanding the working mechanisms of the heart, both under healthy functioning and during disease…
A Robust SINDy Approach by Combining Neural Networks and an Integral Form
Ali Forootani, Pawan Goyal, Peter Benner
The discovery of governing equations from data has been an active field of research for decades. One widely used methodology for this purpose is sparse regression for nonlinear dyn…
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…
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…
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…
Accurate error estimation for model reduction of nonlinear dynamical systems via data-enhanced error closure
Sridhar Chellappa, Lihong Feng, Peter Benner
Accurate error estimation is crucial in model order reduction, both to obtain small reduced-order models and to certify their accuracy when deployed in downstream applications such…