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PINNMorph: Evolving Online Adaptation Policies for Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) provide a learning-based framework for solving partial differential equations (PDEs), yet their training behavior can change substantially throughout optimization. Residual distributions, gradient interactions, regional learning difficulty, and model-capacity
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