Lattice Field Theory

Latest papers

16 papers6 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows
Oct 14, 2026Claudio Bonanno, Andrea Bulgarelli, Elia Cellini +4Lattice Field Theory High-Energy Physics
Albert Einstein Center for Fundamental Physics, Institute for Theoretical Physics, University of Bern, Sidlerstraße 5, CH-3012 Bern, Switzerland · Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions” (TRA Matter) and Helmholtz Institute for Radiation and Nuclear Physics (HISKP), University of Bonn, Nussallee 14-16, 53115 Bonn, Germany · Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Edinburgh EH9 3FD, United Kingdom +3
Data Free Training of Diffusion Models for Lattice Field Theories
Oct 13, 2026Dennis Grinwald, Ankur Singha, Khaled Kahouli +2Lattice Field Theory Diffusion Model Sampling
Berlin Institute for the Foundations of Learning and Data (BIFOLD), Germany · Technische Universität Berlin, Germany · Deutsches Elektronen-Synchrotron (DESY), Platanenallee 6, 15738 Zeuthen, Germany +2
Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery
Oct 8, 2026Xun Chen, Weiyao Ke, Yu-Gang Ma +2Lattice Field Theory High-Energy Physics
School of Nuclear Science and Technology, University of South China, Hengyang 421001, China · INFN — Istituto Nazionale di Fisica Nucleare — Sezione di Bari, Via Orabona 4, 70125 Bari, Italy · Key Laboratory of Quark and Lepton Physics (MOE) & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei 430079, China +6
Sampling SU(N) gauge theory on a 2D lattice from independent plaquettes via holonomies and corner reweighting
Oct 6, 2026Javad KomijaniLattice Field Theory High-Energy Physics
SIMANF: Sample Free Learning of Unnormalized Distributions via Simulated Annealing in Normalizing Flows
Sep 26, 2026Vikas KanaujiaLattice Field Theory Sampling from Unnormalized Distributions
Mahindra University Department of Electrical and Computer Engineering Hyderabad, Telangana, India
Accurate Sampling from Diffusion Models
Sep 24, 2026Dénes SextyLattice Field Theory Diffusion Model Sampling
Institute of Physics, NAWI Graz, University of Graz, Universitätsplatz 5, 8010 Graz, Austria
Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
Jul 17, 2026Moxian QianMarkov Chain Monte Carlo Lattice Field Theory
Helmholtz Institute Mainz Johannes Gutenberg University Mainz Institute of Molecular Biology (IMB) Mainz, Germany
Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories
Jul 8, 2026Tobias Göbel, Julian R. Ebelt, Zier Mensch +2Phase Transitions Neural Network Interpretability
Institute of Physics, University of Amsterdam, Amsterdam, Netherlands · Department of Physics, National Taiwan University, Taipei, Taiwan · Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA +2
Sampling the Schwinger Model with Gauge-Equivariant Diffusion
Jun 25, 2026Octavio Vega, Aida X. El-KhadraDiffusion Sampling Generative Modeling
Department of Physics University of Illinois Urbana-Champaign Urbana, IL 61801
Learning the generating functional for variance reduction in lattice QCD
Jun 14, 2026Ryan Abbott, Yang Fu, Daniel C. Hackett +3High-Energy Physics Variance Reduction
1Physics Department, Columbia University, New York, NY 10027, USA · Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · 3The NSF AI Institute for Artificial Intelligence and Fundamental Interactions +3
Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theory
May 11, 2026Moxian QianFlow Matching Markov Chain Monte Carlo
Johannes Gutenberg University Mainz, 55128 Mainz, Germany
Diffusion model for SU(N) gauge theories
May 7, 2026Javad Komijani, Marina K. Marinkovic, Lara TurgutScore Matching Diffusion Sampling
Institute for Theoretical Physics, ETH Zurich, 8093 Zurich, Switzerland
Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Apr 22, 2026Ali Rayat, Yaohang Li, Gia-Wei ChernMessage Passing Neural Networks High-Energy Physics
Department of Physics, University of Virginia, Charlottesville, VA 22904, USA · Department of Computer Science, Old Dominion University, Norfolk, VA 22904, USA.
Machine learning for four-dimensional SU(3) lattice gauge theories
Apr 14, 2026Urs WengerLattice Field Theory High-Energy Physics
Institute for Theoretical Physics Albert Einstein Center for Fundamental Physics University of Bern CH-3012 Bern Switzerland
Variance reduction in lattice QCD observables via normalizing flows
Mar 3, 2026Ryan Abbott, Denis Boyda, Yang Fu +5Lattice Field Theory Variance Reduction
Physics Department, Columbia University, New York, NY 10027, USA · Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions +3
A Monte Carlo estimator of flow fields for sampling and noise problems
Feb 27, 2026Michael S. Albergo, Gurtej KanwarLattice Field Theory High-Energy Physics
Society of Fellows, Harvard University 78 Mount Auburn St, Cambridge, MA United States · Higgs Centre for Theoretical Physics, School of Physics and Astronomy, University of Edinburgh, EH9 3FD Edinburgh, United Kingdom

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Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows

Claudio Bonanno, Andrea Bulgarelli, Elia Cellini, Alessandro Nada+3

Albert Einstein Center for Fundamental Physics, Institute for Theoretical Physics, University of Bern, Sidlerstraße 5, CH-3012 Bern, Switzerland · Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions” (TRA Matter) and Helmholtz Institute for Radiation and Nuclear Physics (HISKP), University of Bonn, Nussallee 14-16, 53115 Bonn, Germany · Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Edinburgh EH9 3FD, United Kingdom+3

Lattice Field Theory High-Energy Physics Normalizing Flows

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In this work we provide numerical evidence that topological freezing in

\mathrm{SU}(3)

Yang-Mills theory can be mitigated with a flow-based approach, as tested at lattice spacings as fine as 0.03~fm. We do so by analyzing the scaling of the costs of a computational strategy based on the combination of open boundary conditions and Stochastic Normalizing Flows. We show that the integrated autocorrelation time of the squared topological charge and the statistical uncertainty on the topological susceptibility remain under control throughout the explored parameter range with currently available computational resources. This marks an important step toward the use of even more efficient flow-based architectures for ergodic sampling of topology in state-of-the-art lattice gauge theory calculations.