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20222026
most citedDEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks

6 citations · 18 across the 13 of their papers we have counts for

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2 papers · 1 filter

cs.LG2022★ 5 cited

Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows

Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis +1

Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sci…

cs.LG2022★ 6 cited

DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks

Blake Bullwinkel, Dylan Randle, Pavlos Protopapas +1

Solutions to differential equations are of significant scientific and engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving…