6 citations · 39 across the 24 of their papers we have counts for
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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…
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…
Data-Driven Identification of Quadratic Representations for Nonlinear Hamiltonian Systems using Weakly Symplectic Liftings
Süleyman Yildiz, Pawan Goyal, Thomas Bendokat +1
We present a framework for learning Hamiltonian systems using data. This work is based on a lifting hypothesis, which posits that nonlinear Hamiltonian systems can be written as no…