8 citations · 21 across the 14 of their papers we have counts for
16 papers
On the Compatibility of DEIM Subspaces for Nonlinear Forces Reduction in Projective Dynamics
Shaimaa Monem Abdelhafez, Igor Pontes Duff, Peter Benner
In physics-based simulations, the evaluation of nonlinear constraint forces represents a primary source of computational complexity. Within the framework of projective dynamics, th…
An approach to encode divergence-free stress fields in neural approximations based on stress potentials
Mohammad S. Khorrami, Pawan Goyal, Soroush Motahari +5
The purpose of the current work is the development of an approach to account for quasi-static mechanical equilibrium in empirical (i.e., data-based) models for the stress field emp…
Reduced-Order Inference with Structure-Preserving Parametrization for Bending and Rotating Systems
Yevgeniya Filanova, Igor Pontes Duff, Pawan Goyal +1
Mechanical systems are often characterized only by their response to certain loads known from experiments or simulations. The obtained data can be used for various purposes: system…
Inference of Substructured Reduced-Order Models for Dynamic Contact from Contact-free Simulations
Diana Manvelyan-Stroot, Yevgeniya Filanova, Igor Pontes Duff +2
In this paper, we propose an operator-inference-based reduction approach for contact problems, leveraging snapshots from simulations without active contact. Contact problems are so…
A CFL-type Condition and Theoretical Insights for Discrete-Time Sparse Full-Order Model Inference
Leonidas Gkimisis, Süleyman Yıldız, Peter Benner +1
In this work, we investigate the data-driven inference of a discrete-time dynamical system via a sparse Full-Order Model (sFOM). We first formulate the involved Least Squares (LS)…
On the representation of energy-preserving quadratic operators with application to Operator Inference
Leonidas Gkimisis, Igor Pontes Duff, Pawan Goyal +1
In this work, we investigate a skew-symmetric parameterization for energy-preserving quadratic operators. Earlier, [Goyal et al., 2023] proposed this parameterization to enforce en…
Non-intrusive reduced-order modeling for dynamical systems with spatially localized features
Leonidas Gkimisis, Nicole Aretz, Marco Tezzele +3
This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed…
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…