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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
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spelling 2025-04-23T16:10:39.3530151 v2 69012 2025-03-04 Diffusion models and stochastic quantisation in lattice field theory 1ba0dad382dfe18348ec32fc65f3f3de 0000-0002-6038-3782 Gert Aarts Gert Aarts true false 2025-03-04 BGPS 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. Conference Paper/Proceeding/Abstract Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024) 466 037 Sissa Medialab Trieste, Italy 1824-8039 22 1 2025 2025-01-22 10.22323/1.466.0037 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University 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. 2025-04-23T16:10:39.3530151 2025-03-04T11:40:12.2983749 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics Gert Aarts 0000-0002-6038-3782 1 Lingxiao Wang 2 Kai Zhou 3 69012__34072__e8e90f18a96e445db3357eac39ad7029.pdf 69012.VoR.pdf 2025-04-23T16:06:25.7347872 Output 1702957 application/pdf Version of Record true © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). true eng https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
title Diffusion models and stochastic quantisation in lattice field theory
spellingShingle Diffusion models and stochastic quantisation in lattice field theory
Gert Aarts
title_short Diffusion models and stochastic quantisation in lattice field theory
title_full Diffusion models and stochastic quantisation in lattice field theory
title_fullStr Diffusion models and stochastic quantisation in lattice field theory
title_full_unstemmed Diffusion models and stochastic quantisation in lattice field theory
title_sort Diffusion models and stochastic quantisation in lattice field theory
author_id_str_mv 1ba0dad382dfe18348ec32fc65f3f3de
author_id_fullname_str_mv 1ba0dad382dfe18348ec32fc65f3f3de_***_Gert Aarts
author Gert Aarts
author2 Gert Aarts
Lingxiao Wang
Kai Zhou
format Conference Paper/Proceeding/Abstract
container_title Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024)
container_volume 466
container_start_page 037
publishDate 2025
institution Swansea University
issn 1824-8039
doi_str_mv 10.22323/1.466.0037
publisher Sissa Medialab
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Physics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Physics
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description 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.
published_date 2025-01-22T08:21:19Z
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score 11.105427

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