Publications in Scientific Journals:
M. Favoni, A. Ipp, D. Mueller, D. Schuh:
"Lattice gauge equivariant convolutional neural networks";
Physical Review Letters,
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.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Project Head Andreas Ipp:
Project Head Anton Rebhan:
Doktoratskolleg Particles and Interactions
Created from the Publication Database of the Vienna University of Technology.