Paper ID: 2112.11239

Preserving gauge invariance in neural networks

Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh

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.

Submitted: Dec 21, 2021