Speaker
Diaa Eddin Habibi
(Swansea University)
Description
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.
Primary author
Diaa Eddin Habibi
(Swansea University)
Co-authors
Gert Aarts
(Swansea University)
Kai Zhou
(Chinese University of Hong Kong (CUHK))
Lingxiao Wang
(RIKEN)
