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representative citing papers

Diffusion model for SU(N) gauge theories

hep-lat · 2026-05-07 · unverdicted · novelty 6.0

Implicit score matching trains diffusion models that successfully sample SU(3) Wilson gauge configurations on lattices, with a Hamiltonian-dynamics corrector needed for strong coupling.

Machine learning for four-dimensional SU(3) lattice gauge theories

hep-lat · 2026-04-14 · unverdicted · novelty 3.0

Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.

FLAG Review 2024

hep-lat · 2024-11-06 · accept · novelty 2.0

The FLAG 2024 review provides updated averages of lattice QCD determinations for quark masses, decay constants, form factors, mixing parameters, and nucleon matrix elements.

citing papers explorer

Showing 5 of 5 citing papers.

  • Normalizing flows for all-orders QED corrections in lattice field theory hep-lat · 2026-05-21 · unverdicted · none · ref 60

    Normalizing flows enable all-order QED corrections in lattice scalar QED in 2-4 dimensions with reduced variance and transferability from small to large lattices.

  • Diffusion model for SU(N) gauge theories hep-lat · 2026-05-07 · unverdicted · none · ref 7

    Implicit score matching trains diffusion models that successfully sample SU(3) Wilson gauge configurations on lattices, with a Hamiltonian-dynamics corrector needed for strong coupling.

  • Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory hep-lat · 2025-10-29 · unverdicted · none · ref 72

    Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.

  • Machine learning for four-dimensional SU(3) lattice gauge theories hep-lat · 2026-04-14 · unverdicted · none · ref 9

    Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.

  • FLAG Review 2024 hep-lat · 2024-11-06 · accept · none · ref 171

    The FLAG 2024 review provides updated averages of lattice QCD determinations for quark masses, decay constants, form factors, mixing parameters, and nucleon matrix elements.