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20192023
most citedSurrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data

1.1k citations · 2.3k across the 10 of their papers we have counts for

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Showing 2021Show all

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cs.CE2021★ 313 cited

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

Han Gao, Matthew J. Zahr, Jian-Xun Wang

Despite the great promise of the physics-informed neural networks (PINNs) in solving forward and inverse problems, several technical challenges are present as roadblocks for more c…