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Equivariant Graph Mechanics Networks with Constraints
[article]
2022
arXiv
pre-print
Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting systems are exponentially-compositional, symmetrical, and commonly geometrically-constrained. Current methods, particularly the ones based on equivariant Graph Neural Networks (GNNs), have targeted on the first two challenges but remain immature for constrained systems. In this paper, we propose Graph Mechanics Network
arXiv:2203.06442v1
fatcat:ueyqmfnapjgxxovuf3ieohxsb4