Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Broad Physical Dynamics Learning-Reference-Cited by-同舟云学术

Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Broad Physical Dynamics Learning

Published:2024-08-24 Issue: Volume:12363 Page:4548-4558
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zheng Zinan¹^ORCID,Liu Yang²^ORCID,Li Jia¹^ORCID,Yao Jianhua³^ORCID,Rong Yu³^ORCID

Affiliation:

1. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

2. Hong Kong University of Science and Technology & Hong Kong University of Science and Technology (Guangzhou), Hong Kong SAR, China

3. Tencent AI Lab, Shenzhen, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671957

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5. Johannes Brandstetter Rob Hesselink Elise van der Pol Erik J. Bekkers and Max Welling. 2022. Geometric and Physical Quantities improve E(3) Equivariant Message Passing. In ICLR.