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math.NA2025
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…
math.NA2024★ 2 cited
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…