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arXiv · 2012.12901

Lattice gauge equivariant convolutional neural networks

Matteo Favoni·Andreas Ipp·David I. Müller·Daniel Schuh
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Abstract

We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems. At the heart of this network structure is a novel convolutional layer that preserves gauge equivariance while forming arbitrarily shaped Wilson loops in successive bilinear layers. Together with topological information, for example from Polyakov loops, such a network can in principle approximate any gauge covariant function on the lattice. We demonstrate that L-CNNs can learn and generalize gauge invariant quantities that traditional convolutional neural networks are incapable of finding.

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BibTeXRIS

Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh. 2020-12-23. Lattice gauge equivariant convolutional neural networks. https://doi.org/10.1103/physrevlett.128.032003

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