No Cover Image

Conference Paper/Proceeding/Abstract 47 views 12 downloads

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

  • 70968.VoR.pdf

    PDF | Version of Record

    Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

    Download (3.21MB)

Check full text

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...

Full description

Published in: Proceedings of The 41st International Symposium on Lattice Field Theory — PoS(LATTICE2024)
ISSN: 1824-8039
Published: Trieste, Italy Sissa Medialab 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa70968
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 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.
College: Faculty of Science and Engineering
Funders: 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.
Start Page: 042

Similar Items