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Exploring Generative Networks for Manifolds with Non-Trivial Topology

Shiyang Chen, Gert Aarts Orcid Logo, Biagio Lucini

Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024), Volume: 466, Start page: 042

Swansea University Author: Gert Aarts Orcid Logo

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DOI (Published version): 10.22323/1.466.0042

Abstract

The expressive power of neural networks in modelling non-trivial distributions can in principle be exploited to bypass topological freezing and critical slowing down in simulations of lattice field theories. Some popular approaches are unable to sample correctly non-trivial topology, which may lead...

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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/cronfa70968
first_indexed 2025-11-22T22:01:17Z
last_indexed 2026-01-17T05:33:02Z
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spelling 2026-01-16T10:45:26.0492266 v2 70968 2025-11-22 Exploring Generative Networks for Manifolds with Non-Trivial Topology 1ba0dad382dfe18348ec32fc65f3f3de 0000-0002-6038-3782 Gert Aarts Gert Aarts true false 2025-11-22 BGPS The expressive power of neural networks in modelling non-trivial distributions can in principle be exploited to bypass topological freezing and critical slowing down in simulations of lattice field theories. Some popular approaches are unable to sample correctly non-trivial topology, which may lead to some classes of configurations not being generated. In this contribution, we present a novel generative method inspired by a model previously introduced in the ML community (GFlowNets). We demonstrate its efficiency at exploring ergodically configuration manifolds with non-trivial topology through applications such as triple ring models and two-dimensional lattice scalar field theory. Conference Paper/Proceeding/Abstract Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024) 466 042 Sissa Medialab Trieste, Italy 1824-8039 18 12 2025 2025-12-18 10.22323/1.466.0042 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University SYC is supported by the China Scholarship Council (No. 202308420042) and a Swansea University joint PhD project. GA and BL are supported by STFC Consolidated Grant ST/X000648/1. BLis further supported by the UKRI EPSRC ExCALIBUR ExaTEPP project EP/X017168/1. 2026-01-16T10:45:26.0492266 2025-11-22T16:54:35.0941931 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics Shiyang Chen 1 Gert Aarts 0000-0002-6038-3782 2 Biagio Lucini 3 70968__36019__f7b56168af914569aab97fabeecb1279.pdf 70968.VoR.pdf 2026-01-16T10:18:33.0658917 Output 3368344 application/pdf Version of Record true Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Exploring Generative Networks for Manifolds with Non-Trivial Topology
spellingShingle Exploring Generative Networks for Manifolds with Non-Trivial Topology
Gert Aarts
title_short Exploring Generative Networks for Manifolds with Non-Trivial Topology
title_full Exploring Generative Networks for Manifolds with Non-Trivial Topology
title_fullStr Exploring Generative Networks for Manifolds with Non-Trivial Topology
title_full_unstemmed Exploring Generative Networks for Manifolds with Non-Trivial Topology
title_sort Exploring Generative Networks for Manifolds with Non-Trivial Topology
author_id_str_mv 1ba0dad382dfe18348ec32fc65f3f3de
author_id_fullname_str_mv 1ba0dad382dfe18348ec32fc65f3f3de_***_Gert Aarts
author Gert Aarts
author2 Shiyang Chen
Gert Aarts
Biagio Lucini
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 042
publishDate 2025
institution Swansea University
issn 1824-8039
doi_str_mv 10.22323/1.466.0042
publisher Sissa Medialab
college_str Faculty of Science and Engineering
hierarchytype
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
document_store_str 1
active_str 0
description The expressive power of neural networks in modelling non-trivial distributions can in principle be exploited to bypass topological freezing and critical slowing down in simulations of lattice field theories. Some popular approaches are unable to sample correctly non-trivial topology, which may lead to some classes of configurations not being generated. In this contribution, we present a novel generative method inspired by a model previously introduced in the ML community (GFlowNets). We demonstrate its efficiency at exploring ergodically configuration manifolds with non-trivial topology through applications such as triple ring models and two-dimensional lattice scalar field theory.
published_date 2025-12-18T05:34:05Z
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score 11.096295

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