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…