TGSR-PINN improves PINN inverse-problem transfer learning by scoring neuron relevance to the target task via Taylor sensitivity and pre-activation variance, then applying continuous soft decay to low-scoring neurons rather than hard pruning or random resetting.
Self-adaptive weights based on balanced residual decay rate for PINNs and deep operator networks.Journal of Computational Physics, 2025, 542: 114226
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Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach
TGSR-PINN improves PINN inverse-problem transfer learning by scoring neuron relevance to the target task via Taylor sensitivity and pre-activation variance, then applying continuous soft decay to low-scoring neurons rather than hard pruning or random resetting.