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

6 citations · 7 across the 2 of their papers we have counts for

collaborators

2 papers

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…

cs.LG2020★ 1 cited

Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks

Dylan Randle, Pavlos Protopapas, David Sondak

Solutions to differential equations are of significant scientific and engineering relevance. Recently, there has been a growing interest in solving differential equations with neur…