Physics-Guided Port-Graph Learning for Fastener Parameter Identification from Vehicle Responses

Identifying physical parameters from partial observations of coupled dynamical systems is a fundamental inverse problem, yet remains challenging when target effects are entangled with unknown excitation, unobserved states, and nuisance parameter variation. We study this setting in vehicle–track systems, where segment-shared fastener stiffness and damping are inferred from a single short record of vehicle-side responses without direct rail or slab measurements. We first assess local identifiability under track-irregularity and vehicle-parameter variation using profiled Fisher information. The analysis shows that the two target parameters retain distinct sensitivity directions and that their informative cues vary across frequency. Motivated by these observations, we propose a physics-guided port-graph learning framework that combines frequency-resolved magnitude and phase with the known mechanical interaction structure of the coupled system. A typed port graph propagates observed vehicle information through suspension, wheel–rail contact, and fastener interactions while representing unobserved track components as latent nodes. Experiments on simulated single-pass responses show improved parameter identification over strong spectral and graph-based baselines, with the clearest gains under short observation windows. Ablations further confirm the complementary roles of spectral resolution, phase information, and mechanically structured interaction. These results highlight physical interaction structure as an effective inductive bias for inverse identification from partial and temporally limited observations.