Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

Short summary

This paper identifies and quantifies pathological gradient flows in Physics-Informed Neural Networks (PINNs) that hinder training, particularly for stiff dynamics, and proposes a novel adaptive activation function that significantly improves convergence and accuracy.

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