March 13, 2024, 4:43 a.m. | Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, Yu Rong

cs.LG updates on

arXiv:2308.13212v2 Announce Type: replace
Abstract: Graph Neural Networks (GNNs) with equivariant properties have emerged as powerful tools for modeling complex dynamics of multi-object physical systems. However, their generalization ability is limited by the inadequate consideration of physical inductive biases: (1) Existing studies overlook the continuity of transitions among system states, opting to employ several discrete transformation layers to learn the direct mapping between two adjacent states; (2) Most models only account for first-order velocity information, despite the fact that many …

abstract arxiv biases continuity cs.lg dynamics gnns graph graph neural networks however inductive modeling networks neural networks object studies systems tools transitions type

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