Rigid-deformation decomposition AI framework for 3D spatio-temporal prediction of vehicle collision dynamics

초록

This study presents a rigid-deformation decomposition framework for vehicle collision dynamics that mitigates the limited representation of high-frequency deformation modes in implicit neural representations, i.e., coordinate-based neural networks that directly map spatio-temporal coordinates to physical fields. We introduce a rigid-deformation decomposition architecture that decouples global rigid-body motion from local deformation using two scale-specific networks, denoted as RigidNet and DeformationNet. To enforce kinematic separation between the two components, we adopt a frozen-anchor formulation combined with a quaternionincremental scheme. This strategy alleviates the kinematic instability observed in joint training and yields a 29.8% reduction in rigid-body motion error compared with conventional undecomposed prediction. The stable rigid-body anchor improves the resolution of high-frequency structural buckling, which leads to a 17.2% reduction in the total interpolation error. Optimization analysis indicates that the decomposition smooths the optimization surface, which enhances robustness to distribution shifts in angular extrapolation and yields a 46.6% reduction in error. To assess physical validity beyond numerical accuracy, we benchmark the decomposed components against an oracle model that represents an upper bound on performance. The proposed framework recovers 92% of the directional correlation between rigid and deformation components and 96% of the spatial deformation localization accuracy relative to the oracle, while tracking the temporal energy dynamics with an 8 ms delay. These results demonstrate that rigid-deformation decomposition enables accurate and physically consistent predictions for nonlinear collision dynamics.

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