PINNfluence: Interpreting PINNs through Influence Functions

ArXi:2409.08958v3 Announce Type: replace Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their behavior remains largely opaque and is typically understood through failure mode analyses rather than explicit interpretability. To address this issue, we