136 citations · 143 across the 13 of their papers we have counts for
13 papers
LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks
Nilay Anurag, Shital Adhikari, Taniya Kapoor +1
Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE). However, PINNs have been shown to pe…
Curvature-aware dynamic precision approach for physics-informed neural networks
Yingjie Shao, Ioannis N. Athanasiadis, George van Voorn +1
Physics-informed neural networks (PINNs) have become a promising framework for simulating partial differential equations (PDEs) by embedding physical laws directly into neural netw…
Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers
Abhishek Chandra, Taniya Kapoor
Solving time-dependent partial differential equations (PDEs) is an important problem in computational science and engineering. Physics-informed neural networks (PINNs) learn PDE so…
Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations
Eva van Tegelen, Taniya Kapoor, George A. K. van Voorn +2
Developing neural operators that accurately predict the behavior of systems governed by partial differential equations (PDEs) across unseen parameter regimes is crucial for robust…
Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
Alexander Heinlein, Taniya Kapoor
Approximating the solutions of boundary value problems governed by partial differential equations with neural networks is challenging, largely due to the difficult training process…
Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems
Abhishek Chandra, Taniya Kapoor, Mitrofan Curti +2
Complex piezoelectric systems are foundational in industrial applications. Their performance, however, is challenged by the nonlinear voltage-displacement hysteretic relationships.…
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
Taniya Kapoor, Abhishek Chandra, Anastasios Stamou +1
Real-world systems, from aerospace to railway engineering, are modeled with partial differential equations (PDEs) describing the physics of the system. Estimating robust solutions…
Fast training of accurate physics-informed neural networks without gradient descent
Chinmay Datar, Taniya Kapoor, Abhishek Chandra +6
Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a p…
Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations
Taniya Kapoor, Hongrui Wang, Alfredo Nunez +1
This paper introduces a novel methodology for simulating the dynamics of beams on elastic foundations. Specifically, Euler-Bernoulli and Timoshenko beam models on the Winkler found…
Neural oscillators for magnetic hysteresis modeling
Abhishek Chandra, Taniya Kapoor, Bram Daniels +4
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We d…
Neural oscillators for generalization of physics-informed machine learning
Taniya Kapoor, Abhishek Chandra, Daniel M. Tartakovsky +3
A primary challenge of physics-informed machine learning (PIML) is its generalization beyond the training domain, especially when dealing with complex physical problems represented…
Physics-informed machine learning for moving load problems
Taniya Kapoor, Hongrui Wang, Alfredo Núñez +1
This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs)…
Physics-informed neural networks for solving forward and inverse problems in complex beam systems
Taniya Kapoor, Hongrui Wang, Alfredo Nunez +1
This paper proposes a new framework using physics-informed neural networks (PINNs) to simulate complex structural systems that consist of single and double beams based on Euler-Ber…