6 citations · 39 across the 24 of their papers we have counts for
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Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flows
Peter Benner, Pawan Goyal, Jan Heiland +1
Reduced-order modeling has a long tradition in computational fluid dynamics. The ever-increasing significance of data for the synthesis of low-order models is well reflected in the…
Data-Driven Learning of Reduced-order Dynamics for a Parametrized Shallow Water Equation
Süleyman Yıldız, Pawan Goyal, Peter Benner +1
This paper discusses a non-intrusive data-driven model order reduction method that learns low-dimensional dynamical models for a parametrized shallow water equation. We consider th…
A Non-Intrusive Method to Inferring Linear Port-Hamiltonian Realizations using Time-Domain Data
Karim Cherifi, Pawan Goyal, Peter Benner
Port-Hamiltonian systems have gained a lot of attention in recent years due to their inherent valuable properties in modeling and control. In this paper, we are interested in const…
Low-Rank and Total Variation Regularization and Its Application to Image Recovery
Pawan Goyal, Hussam Al Daas, Peter Benner
In this paper, we study the problem of image recovery from given partial (corrupted) observations. Recovering an image using a low-rank model has been an active research area in da…
Low-dimensional approximations of high-dimensional asset price models
Martin Redmann, Christian Bayer, Pawan Goyal
We consider high-dimensional asset price models that are reduced in their dimension in order to reduce the complexity of the problem or the effect of the curse of dimensionality in…
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
Peter Benner, Pawan Goyal, Boris Kramer +2
This work presents a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that…