activity
20172021
most citedIdentification of Dominant Subspaces for Linear Structured Parametric Systems and Model Reduction

6 citations · 14 across the 4 of their papers we have counts for

collaborators

12 papers

cs.LG20215 cited

LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes

Pawan Goyal, Peter Benner

Mathematical modeling is an essential step, for example, to analyze the transient behavior of a dynamical process and to perform engineering studies such as optimization and contro…

math.DS2020

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…

math.NA2020

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…

eess.SY2020

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…

cs.CV2020

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…

math.PR2020

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…