8 citations · 21 across the 14 of their papers we have counts for
9 papers · 1 filter
Structure-preserving learning for multi-symplectic PDEs
Süleyman Yıldız, Pawan Goyal, Peter Benner
This paper presents an energy-preserving machine learning method for inferring reduced-order models (ROMs) by exploiting the multi-symplectic form of partial differential equations…
Active Sampling of Interpolation Points to Identify Dominant Subspaces for Model Reduction
Celine Reddig, Pawan Goyal, Igor Pontes Duff +1
Model reduction is an active research field to construct low-dimensional surrogate models of high fidelity to accelerate engineering design cycles. In this work, we investigate mod…
A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids
Mohammad S. Khorrami, Pawan Goyal, Jaber R. Mianroodi +3
The purpose of the current work is the development of a so-called physics-encoded Fourier neural operator (PeFNO) for surrogate modeling of the quasi-static equilibrium stress fiel…
GS-PINN: Greedy Sampling for Parameter Estimation in Partial Differential Equations
Ali Forootani, Harshit Kapadia, Sridhar Chellappa +2
Partial differential equation parameter estimation is a mathematical and computational process used to estimate the unknown parameters in a partial differential equation model from…
GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations
Ali Forootani, Harshit Kapadia, Sridhar Chellappa +2
The sparse identification of nonlinear dynamical systems (SINDy) is a data-driven technique employed for uncovering and representing the fundamental dynamics of intricate systems b…
Stability-Certified Learning of Control Systems with Quadratic Nonlinearities
Igor Pontes Duff, Pawan Goyal, Peter Benner
This work primarily focuses on an operator inference methodology aimed at constructing low-dimensional dynamical models based on a priori hypotheses about their structure, often in…
Balanced Truncation of Descriptor Systems with a Quadratic Output
Jennifer Przybilla, Igor Pontes Duff, Pawan Goyal +1
This work discusses model reduction for differential-algebraic systems with quadratic output equations. Under mild conditions, these systems can be transformed into a Weierstraß ca…
Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
Ion Victor Gosea, Luisa Peterson, Pawan Goyal +3
In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…