NeuMC -- a package for neural sampling for lattice field theories
High Energy Physics - Lattice
2025-10-09 v1 Machine Learning
Abstract
We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly popular in the context of Monte-Carlo simulations as they can effectively approximate target probability distributions, possibly alleviating some shortcomings of the Markov chain Monte-Carlo methods. Our package provides tools to create such samplers for two-dimensional field theories.
Cite
@article{arxiv.2503.11482,
title = {NeuMC -- a package for neural sampling for lattice field theories},
author = {Piotr Bialas and Piotr Korcyl and Tomasz Stebel and Dawid Zapolski},
journal= {arXiv preprint arXiv:2503.11482},
year = {2025}
}
Comments
42 pages, 15 figures, for associated code repository, see https://github.com/nmcmc/neumc.git