AI RESEARCH
Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
arXiv CS.LG
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ArXi:2602.19967v3 Announce Type: replace Physics-informed neural networks (PINNs) provide a promising framework for solving inverse problems governed by partial differential equations (PDEs) by integrating observational data and physical constraints in a unified optimization objective. However, the ill-posed nature of PDE inverse problems makes them highly sensitive to noise. Even a small fraction of corrupted observations can distort internal neural representations, severely impairing accuracy and destabilizing