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20182026
most citedFinite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

363 citations · 462 across the 10 of their papers we have counts for

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Showing 2023 · math.NAShow all

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math.NA2023★ 96 cited

Multilevel domain decomposition-based architectures for physics-informed neural networks

Victorita Dolean, Alexander Heinlein, Siddhartha Mishra +1

Physics-informed neural networks (PINNs) are a powerful approach for solving problems involving differential equations, yet they often struggle to solve problems with high frequenc…