EPJ Web Conf.
Volume 258, 2022A Virtual Tribute to Quark Confinement and the Hadron Spectrum (vConf21)
|Number of page(s)||8|
|Section||Parallel Presentations Track H|
|Published online||11 January 2022|
Preserving gauge invariance in neural networks
1 Institute for Theoretical Physics, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria
2 Speaker and corresponding author
Published online: 11 January 2022
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.
© The Authors, published by EDP Sciences, 2022
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