MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory
High Energy Physics - Lattice
2023-12-19 v2
Abstract
We present a trainable framework for efficiently generating gauge configurations, and discuss ongoing work in this direction. In particular, we consider the problem of sampling configurations from a 4D lattice gauge theory, and consider a generalized leapfrog integrator in the molecular dynamics update that can be trained to improve sampling efficiency. Code is available online at https://github.com/saforem2/l2hmc-qcd.
Cite
@article{arxiv.2312.08936,
title = {MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory},
author = {Sam Foreman and Xiao-Yong Jin and James C. Osborn},
journal= {arXiv preprint arXiv:2312.08936},
year = {2023}
}