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Diffusion models and stochastic quantisation in lattice field theory

Gert Aarts Orcid Logo, Lingxiao Wang, Kai Zhou

Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024), Volume: 466, Start page: 037

Swansea University Author: Gert Aarts Orcid Logo

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DOI (Published version): 10.22323/1.466.0037

Abstract

Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochastic quantisation in field theory and employ it to generate configurations for scalar fields on a two-dimensional lattice....

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Published in: Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024)
ISSN: 1824-8039
Published: Trieste, Italy Sissa Medialab 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69012
Abstract: Diffusion models are currently the leading generative AI approach used for image generation in e.g. DALL-E and Stable Diffusion. In this talk we relate diffusion models to stochastic quantisation in field theory and employ it to generate configurations for scalar fields on a two-dimensional lattice. We end with some speculations on possible applications.
College: Faculty of Science and Engineering
Funders: GA is supported by STFC Consolidated Grant ST/X000648/1. LW thanks the DEEP-IN working group at RIKEN-iTHEMS for support. KZ is supported by the CUHK-Shenzhen University development fund under grant No. UDF01003041 and UDF03003041, and Shenzhen Peacock fund under No. 2023TC0179.
Start Page: 037

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