Speaker
Diaa Eddin Habibi
(Swansea University)
Description
Complex langevin for theories with a sign problem effectively sample from a real-valued probability distribution that is a priori unknown and notoriously hard to predict. In generative AI, diffusion models can learn distributions from data. In this contribution, we investigate their ability to capture the distributions sampled by a complex Langevin process, comparing score-based and energy-based approaches, and outlining potential applications.
| Parallel Session (for talks only) | Algorithms and artificial intelligence |
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Author
Diaa Eddin Habibi
(Swansea University)
Co-authors
Prof.
Gert Aarts
(Swansea University)
Prof.
Kai Zhou
(Chinese University of Hong Kong - Shenzhen (CUHK-Shenzhen))
Dr
Lingxiao Wang
(RIKEN)
