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arXiv · 2409.08958

PINNfluence: Interpreting PINNs through Influence Functions

Aleksander Krasowski·Jonas R. Naujoks·Moritz Weckbecker·Galip Ü. Yolcu·Thomas Wiegand·Sebastian Lapuschkin·Wojciech Samek·René P. Klausen
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Abstract

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 introduce PINNfluence, a training data attribution framework for interpreting PINNs based on influence functions. By extending influence functions to composite physics-informed training objectives, we enable fine-grained attribution between predictions, loss components, and training data points. Through benchmark experiments across various PDEs, we demonstrate that influence patterns provide granular diagnostics that distinguish structural characteristics across well-trained and poorly-trained PINNs. PINNfluence thus opens a new avenue for understanding and improving the reliability of PINNs through the lens of their data.

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Aleksander Krasowski, Jonas R. Naujoks, Moritz Weckbecker, Galip Ü. Yolcu, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek, René P. Klausen. 2024-09-13. PINNfluence: Interpreting PINNs through Influence Functions. https://arxiv.org/abs/2409.08958

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