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Learning Physical Dynamics with Subequivariant Graph Neural Networks Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics However, they still encounter several challenges: 1) Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization and should be incorporated into the model design
Learning Physical Dynamics with Subequivariant Graph Neural Networks - NIPS Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics However, they still encounter several challenges: 1) Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization and should be incorporated into the model design
Learning Physical Dynamics with Subequivariant Graph Neural Networks . . . We propose Subequivariant Graph Neural Network (SGNN) that jointly leverages object-aware information as well as subequivariance, a novel concept that relaxes E ( 3 ) -equivariance constraint in the presence of external fields like gravity
arXiv:2210. 06876v1 [cs. LG] 13 Oct 2022 Amherst MIT-IBM Watson AI Lab Abstract Graph Neural Networks (GNNs) have become a prevailin tool for learning physi-cal dynamics However, they still encounter several challenges: 1) Physical laws abide by symmetry, which is a vital inductive bias accounting for model generaliza-tion and should
Learning Physical Dynamics with Subequivariant Graph Neural Networks To tackle these difficulties, we propose a novel backbone, Subequivariant Graph Neural Network, which 1) relaxes equivariance to subequivariance by considering external fields like gravity, where the universal approximation ability holds theoretically; 2) introduces a new subequivariant object-aware message passing for learning physical
Learning Physical Dynamics with Subequivariant Graph Neural Networks We propose Subequivariant Graph Neural Networks for modeling physical dynamics of multiple interacting objects We inject appropriate symmetry into a hierarchical message passing framework, and takes into account both particle- and object-level state messages
Jiaqi Han - Google Scholar Proceedings of the 27th ACM SIGKDD conference on knowledge discovery data … J Han, J Cen, L Wu, Z Li, X Kong, R Jiao, Z Yu, T Xu, F Wu, Z Wang, H Xu, M Arriola, A Gokaslan, JT Chiu, Z
Learning Physical Dynamics with Subequivariant Graph Neural Networks A novel backbone, Subequivariant Graph Neural Network, is proposed, which relaxes equivariance to subequivariance by considering external fields like gravity, where the universal approximation ability holds theoretically