# 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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## 14 Citations

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We review a novel neural network architecture called lattice gauge equivariant convolutional neural networks (L-CNNs), which can be applied to generic machine learning problems in lattice gauge…

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