From the 1 of 8 linked papers with an AI index.
2 citations · 2 across the 5 of their papers we have counts for
8 papers
LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks
Nilay Anurag, Shital Adhikari, Taniya Kapoor +1
The paper introduces LIGO-PINN, a learned weight initialization method using gated layerwise optimization to improve the training stability and convergence of physics-informed neur…
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…
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…
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…