Diffusion models learn distributions generated by complex Langevin dynamics
D.E. Habibi*,
G. Aarts,
L. Wang and
K. Zhou*: corresponding author
Pre-published on:
January 13, 2025
Published on:
—
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.
DOI: https://doi.org/10.22323/1.466.0039
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