SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks

John Anticev, Ali Aghdaei, Wuxinlin Cheng +1

SGM-PINN is a graph-based importance sampling framework to improve the training efficacy of Physics-Informed Neural Networks (PINNs) on parameterized problems. By applying a graph…