AI RESEARCH
Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
arXiv CS.LG
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ArXi:2605.10136v1 Announce Type: new 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 optimization. Existing remedies typically treat this pathology either through scalar loss balancing or full-parameter-space gradient surgery, leaving it unclear which intervention is most appropriate. We show that PINN gradient conflict is not a uniform failure mode with one universal remedy.