Web: http://arxiv.org/abs/2209.07081

Sept. 16, 2022, 1:11 a.m. | Blake Bullwinkel, Dylan Randle, Pavlos Protopapas, David Sondak

cs.LG updates on arXiv.org arxiv.org

Solutions to differential equations are of significant scientific and
engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as
a promising method for solving differential equations, but they lack a
theoretical justification for the use of any particular loss function. This
work presents Differential Equation GAN (DEQGAN), a novel method for solving
differential equations using generative adversarial networks to "learn the loss
function" for optimizing the neural network. Presenting results on a suite of
twelve ordinary and partial differential equations, including …

arxiv function generative adversarial networks loss networks

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