A Graph-Based Inspection and Intervention Tool for Assessing Mechanistic Learning in PINNs

Excerpt

arXiv:2610.04939v1 Announce Type: cross Abstract: We ask whether physically meaningful correspondences discovered inside a trained scientific model remain meaningful outside the conditions under which they were discovered. We introduce GIIT (Graph-based Inspection and Intervention Tool), which represents governing physics as a computational physics dependency graph, maps graph nodes to internal network components via sensitivity- and trend-based discovery, and tests the resulting mapping under t