Preserving gauge invariance in neural networks

• URL: http://arxiv.org/abs/2112.11239v1
• Date: Tue, 21 Dec 2021 14:08:12 GMT
• Title: Preserving gauge invariance in neural networks
• Authors: Matteo Favoni, Andreas Ipp, David I. M\"uller, Daniel Schuh
• Abstract summary: lattice gauge equivariant convolutional neural networks (L-CNNs) We show how L-CNNs can represent a large class of gauge invariant and equivariant functions on the lattice.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In these proceedings we present lattice gauge equivariant convolutional neural networks (L-CNNs) which are able to process data from lattice gauge theory simulations while exactly preserving gauge symmetry. We review aspects of the architecture and show how L-CNNs can represent a large class of gauge invariant and equivariant functions on the lattice. We compare the performance of L-CNNs and non-equivariant networks using a non-linear regression problem and demonstrate how gauge invariance is broken for non-equivariant models.

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