most citedGradient-enhanced physics-informed neural networks for forward and inverse PDE problems

714 citations · 714 across the 1 of their papers we have counts for

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1 paper

cs.LG2021★ 714 cited

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Jeremy Yu, Lu Lu, Xuhui Meng +1

Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual…