[Submitted on 2 Sep 2026]
Title:TRACE: Spatiotemporal Contact Memory Graph Network Simulator for Granular Dynamics
View a PDF of the paper titled TRACE: Spatiotemporal Contact Memory Graph Network Simulator for Granular Dynamics, by Changjian Zhou and 5 other authors
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Abstract:Learned graph simulators provide an efficient alternative to high-fidelity solvers for granular dynamics. However, granular motion depends strongly on inter-granular contact history, which is difficult to preserve when particle contacts form, break, and rearrange. Existing simulators mainly store temporal information in node features or node-level memory. Here we introduce TRACE, a graph-network simulator that stores interaction history directly on contact edges. Each edge maintains a persistent memory updated by attention-based message passing and a gated recurrent unit, while an edge-identity dictionary preserves this memory as the contact graph changes. A physics-structured decoder predicts inter-granular normal and tangential contact forces, enforces the Coulomb friction limit, and applies equal-and-opposite internal forces. The model is trained with single-step pretraining followed by autoregressive rollout fine-tuning. We evaluate TRACE on 2D and 3D granular column-collapse benchmarks. In both cases, TRACE produces stable, physically consistent long-horizon rollouts, closely reproducing the final deposit geometry and the kinetic energy released during collapse. Compared with graph network simulator (GNS) and node-memory graph neural simulator (NMGNS), TRACE reduces long-rollout position error by 31-62% and final-deposit error by 58-89% across the two benchmarks, while using fewer parameters and maintaining near-zero particle interpenetration. TRACE also achieves 12.2$\times$ and 8.9$\times$ speedups over the material point method (MPM) reference solver in 2D and 3D, respectively. Our code is available at this https URL.
Subjects:
Machine Learning (cs.LG); Numerical Analysis (math.NA); Machine Learning (stat.ML)
Cite as: arXiv:2609.02991 [cs.LG]
(or arXiv:2609.02991v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.02991
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Changjian Zhou [view email] [v1] Wed, 2 Sep 2026 15:30:41 UTC (23,476 KB)
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