AIBearisharXiv – CS AI · 6h ago7/10
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Silent Failures in Physics-Informed Neural Networks: Parameter Poisoning and the Limits of Loss-Based Validation
Researchers demonstrate that Physics-Informed Neural Networks (PINNs) can achieve low training loss while producing wildly inaccurate solutions when underlying PDE parameters are corrupted, revealing a critical gap between loss minimization and physical correctness. The study proposes a post-hoc defense mechanism that sweeps residual loss across parameter values to recover true parameters without retraining, offering a practical solution across multiple PDE systems and network architectures.