Contributions to Proceedings:
A. Ipp, D. Mueller, M. Favoni, D. Schuh:
"Preserving gauge invariance in neural networks";
in: "A Virtual Tribute to Quark Confinement and the Hadron Spectrum (vConf21)",
258;
EPJ Web of Conferences,
2022,
ISSN: 2100-014x,
Paper ID 09004,
8 pages.
English abstract:
In these proceedings we present lattice gauge equivariant convolutional neural networks (L-CNNs) which are able to process data from lattice gauge theory simulations while exactly preserving gauge symmetry. We review aspects of the architecture and show how L-CNNs can represent a large class of gauge invariant and equivariant functions on the lattice. We compare the performance of L-CNNs and non-equivariant networks using a non-linear regression problem and demonstrate how gauge invariance is broken for non-equivariant models.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1051/epjconf/202225809004
Related Projects:
Project Head Gerhard Kahl:
Glasma-ML
Created from the Publication Database of the Vienna University of Technology.