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arXiv Machine Learning By Changjian Zhou, Negin Yousefpour, Jie Qi, Junfeng Fang, Guillermo A. Narsilio, Hans Petter Jostad

TRACE: Spatiotemporal Contact Memory Graph Network Simulator for Granular Dynamics

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TRACE is a graph‑network simulator that stores granular contact history on edges using attention‑based message passing and a gated recurrent unit, with an edge‑identity dictionary to preserve memory as contacts change. It predicts normal and tangential forces while enforcing Coulomb friction and internal force balance, and is trained via single‑step pretraining followed by autoregressive fine‑tuning. On 2D and 3D granular column‑collapse benchmarks, TRACE achieves significantly lower long‑rollout position and deposit errors than existing simulators, uses fewer parameters, maintains near‑zero particle interpenetration, and outperforms the material point method by 12.2× (2D) and 8.9× (3D).

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