Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
arXiv:2605.10136v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) train a single neural approximation by minimizing multiple physics- and data-derived losses, but the gradients of these losses often interfere and can stall optim…