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M. Favoni, A. Ipp, D. Mueller, D. Schuh:
"Lattice gauge equivariant convolutional neural networks";
Physical Review Letters, 128 (2022), S. 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 lattice. We demonstrate that L-CNNs can learn and generalize gauge invariant quantities that traditional convolutional neural networks are incapable of finding.

http://dx.doi.org/10.1103/PhysRevLett.128.032003

Projektleitung Gerhard Kahl:
Glasma-ML

Projektleitung Anton Rebhan:
Doktoratskolleg Particles and Interactions