arXiv Machine Learning September 29, 2026 By Thomas Borsani, Giuseppe Di Fatta

Gradient Surgery for Physics-Informed Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces PAM-GS, a physics-aware gradient surgery technique for Physics-Informed Neural Networks (PINNs). It addresses the highly imbalanced multi-task optimisation problem in PINNs by adaptively mitigating task interference based on observed gradient conflicts. Experiments on four PDE benchmarks show that PAM-GS achieves competitive solution accuracy while maintaining strong task-balanced performance, outperforming existing methods on most problems.

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