Diffusion models learn distributions generated by complex Langevin dynamics
High Energy Physics - Lattice
2024-12-05 v1 Machine Learning
Abstract
The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process.
Cite
@article{arxiv.2412.01919,
title = {Diffusion models learn distributions generated by complex Langevin dynamics},
author = {Diaa E. Habibi and Gert Aarts and Lingxiao Wang and Kai Zhou},
journal= {arXiv preprint arXiv:2412.01919},
year = {2024}
}
Comments
8 pages + references. Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024), July 28th - August 3rd, 2024, University of Liverpool, UK