A method to scale second-order training for PINNs, based on domain decomposition and adversarial adaptive sampling.

This work identifies gradient conflicts as a key challenge in training PINNs and shows that quasi second-order methods—especially SOAP—effectively resolve them, leading to 2-10x accuracy gains on 10 PDE benchmarks.