Abstract
In this Letter, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov chain Monte Carlo (MCMC) methods are problematic. More specifically, we show that generative models can be used to estimate the absolute value of the free energy, which is in contrast to existing MCMC-based methods, which are limited to only estimate free energy differences. We demonstrate the effectiveness of the proposed method for two-dimensional theory and compare it to MCMC-based methods in detailed numerical experiments.
- Received 17 July 2020
- Revised 14 October 2020
- Accepted 14 December 2020
- Corrected 21 January 2021
DOI:https://doi.org/10.1103/PhysRevLett.126.032001
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Corrections
21 January 2021
Correction: A proof change request for the fourth affiliation was misinterpreted and has been set right.