e3nn: Euclidean Neural Networks

• URL: http://arxiv.org/abs/2207.09453v1
• Date: Mon, 18 Jul 2022 21:19:40 GMT
• Title: e3nn: Euclidean Neural Networks
• Authors: Mario Geiger and Tess Smidt
• Abstract summary: e3nn is a framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. These core operations of e3nn can be used to efficiently articulate Field Networks, 3D Steerable CNNs, Clebsch-Gordan Networks, SE(3) Transformers and other E(3) equivariant networks.
• Score: 3.231986804142223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. The core of e3nn are equivariant operations such as the TensorProduct class or the spherical harmonics functions that can be composed to create more complex modules such as convolutions and attention mechanisms. These core operations of e3nn can be used to efficiently articulate Tensor Field Networks, 3D Steerable CNNs, Clebsch-Gordan Networks, SE(3) Transformers and other E(3) equivariant networks.

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