Lattice Gauge Equivariant Convolutional Neural Networks
Dec 23, 2020
6 pages
Published in:
- Phys.Rev.Lett. 128 (2022) 3, 3
- Published: Jan 20, 2022
e-Print:
- 2012.12901 [hep-lat]
DOI:
- 10.1103/PhysRevLett.128.032003 (publication)
View in:
Citations per year
Abstract: (APS)
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.Note:
- letter: 6 pages, 5 figures; supplementary material: 14 pages, 4 figures; replaced some figures, added supplementary material
- gauge: covariance
- invariance: gauge
- lattice
- neural network
- lattice field theory
- gauge field theory
- Polyakov loop
- topological
- Wilson loop
- structure
References(57)
Figures(11)
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