Lattice gauge equivariant convolutional neural networks

@article{Favoni2022LatticeGE,
  title={Lattice gauge equivariant convolutional neural networks},
  author={Matteo Favoni and Andreas Ipp and David I. M{\"u}ller and Daniel Schuh},
  journal={Physical review letters},
  year={2022},
  volume={128 3},
  pages={
          032003
        }
}
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… 
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15 Citations

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