Abstract:Graph neural networks (GNNs) have emerged as a versatile and efficient option for modeling the dynamic behavior of deformable materials. While GNNs generalize readily to arbitrary shapes, mesh topologies, and material parameters, existing architectures struggle to correctly predict the temporal evolution of key physical quantities such as linear and angular momentum. In this work, we propose MomentumGNN -- a novel architecture designed to accurately track momentum by construction. Unlike existing GNNs that output unconstrained nodal accelerations, our model predicts per-edge stretching and bending impulses which guarantee the preservation of linear and angular momentum. We train our network in an unsupervised fashion using a physics-based loss, and we show that our method outperforms baselines in a number of common scenarios where momentum plays a pivotal role.
| Comments: | Accepted to 3DV 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Graphics (cs.GR) |
| Cite as: | arXiv:2604.26097 [cs.LG] |
| (or arXiv:2604.26097v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26097 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiahong Wang [view email]
[v1]
Tue, 28 Apr 2026 20:19:33 UTC (12,832 KB)