Related papers: Relaxed Equivariant Graph Neural Networks

Relaxed Equivariant Graph Neural Networks

URL: http://arxiv.org/abs/2407.20471v1
Date: Tue, 30 Jul 2024 00:16:50 GMT
Title: Relaxed Equivariant Graph Neural Networks
Authors: Elyssa Hofgard, Rui Wang, Robin Walters, Tess Smidt,
Abstract summary: 3D Euclidean symmetry equivariant neural networks have demonstrated notable success in modeling complex physical systems. We introduce a framework for relaxed $E(3)$ graph equivariant neural networks that can learn and represent symmetry breaking within continuous groups.
Score: 16.98061967654925
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 3D Euclidean symmetry equivariant neural networks have demonstrated notable success in modeling complex physical systems. We introduce a framework for relaxed $E(3)$ graph equivariant neural networks that can learn and represent symmetry breaking within continuous groups. Building on the existing e3nn framework, we propose the use of relaxed weights to allow for controlled symmetry breaking. We show empirically that these relaxed weights learn the correct amount of symmetry breaking.

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