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20212023
most citedCharacterizing possible failure modes in physics-informed neural networks

118 citations · 334 across the 18 of their papers we have counts for

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physics.comp-ph2023★ 100 cited

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions

Michael Penwarden, Ameya D. Jagtap, Shandian Zhe +2

Physics-informed neural networks (PINNs) as a means of solving partial differential equations (PDE) have garnered much attention in the Computational Science and Engineering (CS&E)…