activity
20172023
most citedExample Setups of Navier-Stokes Equations with Control and Observation: Spatial Discretization and Representation via Linear-quadratic Matrix Coefficients

12 citations · 42 across the 29 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

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

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.NA20231 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

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…

cs.LG2023

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…