Lattice gauge symmetry in neural networks

• URL: http://arxiv.org/abs/2111.04389v1
• Date: Mon, 8 Nov 2021 11:20:11 GMT
• Title: Lattice gauge symmetry in neural networks
• Authors: Matteo Favoni, Andreas Ipp, David I. M\"uller, Daniel Schuh
• Abstract summary: We review a novel neural network architecture called lattice gauge equivariant convolutional neural networks (L-CNNs) We discuss the concept of gauge equivariance which we use to explicitly construct a gauge equivariant convolutional layer and a bilinear layer. The performance of L-CNNs and non-equivariant CNNs is compared using seemingly simple non-linear regression tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We review a novel neural network architecture called lattice gauge equivariant convolutional neural networks (L-CNNs), which can be applied to generic machine learning problems in lattice gauge theory while exactly preserving gauge symmetry. We discuss the concept of gauge equivariance which we use to explicitly construct a gauge equivariant convolutional layer and a bilinear layer. The performance of L-CNNs and non-equivariant CNNs is compared using seemingly simple non-linear regression tasks, where L-CNNs demonstrate generalizability and achieve a high degree of accuracy in their predictions compared to their non-equivariant counterparts.

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