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 𝑆𝑈(3) 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.
Volume 453 -
The 40th International Symposium on Lattice Field Theory (LATTICE2023) -
Algorithms and Artificial Intelligence
MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory
Full text:
pdf
Pre-published on:
December 27, 2023
Published on:
November 06, 2024
Abstract
DOI: https://doi.org/10.22323/1.453.0036
How to cite
Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.
Open Access
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.