Gradient-Update Mismatch: Rethinking Conflict-Free Training of Physics-Informed Neural Networks

Paper recorded by Signals 4 on 2026-09-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-01 on arXiv · recorded by Signals 4 on 2026-09-02

Category: cs.LG · 机器学习 · first seen 2026-09-02

Abstract

Training Physics-Informed Neural Networks (PINNs) requires jointly optimizing physics residual and initial/boundary condition loss terms, which often induce conflicting gradients. Gradient surgery methods mitigate this issue by constructing directions from loss-specific gradients to reduce conflict before optimizer transformation. However, even when the constructed direction is conflict-free, this

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