AI RESEARCH
Crash Assessment via Mesh-Based Graph Neural Networks and Physics-Aware Attention
arXiv CS.AI
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ArXi:2605.11784v1 Announce Type: cross Full-vehicle crash simulations are computationally expensive, limiting their use in iterative design exploration. This work investigates learned hybrid surrogate models (MeshTransolver, MeshGeoTransolver, and MeshGeoFLARE) for predicting time-resolved structural deformation fields in an industrial lateral pole-impact benchmark. We evaluate whether neural surrogates can reproduce full-field crash kinematics with sufficient accuracy, spatial regularity, and structural plausibility for engineering interpretation.