A coupling-flow global proposal for Monte Carlo sampling in 2D pure SU(2) lattice gauge theory is shown to be formally valid and to reproduce the target ensemble in proof-of-principle tests, with modest hybrid gains but no clear outperformance over local baselines.
Kanwar, Flow-based sampling for lattice field theories, in40th International Symposium on Lattice Field Theory (2024) arXiv:2401.01297 [hep-lat]
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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.
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.
Metadynamics bias potentials and volume-extrapolation strategies reduce integrated autocorrelation times of topological charge in lattice gauge theories.
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Flow-Based Global Proposals for Monte Carlo Sampling in SU(2) Lattice Gauge Theory
A coupling-flow global proposal for Monte Carlo sampling in 2D pure SU(2) lattice gauge theory is shown to be formally valid and to reproduce the target ensemble in proof-of-principle tests, with modest hybrid gains but no clear outperformance over local baselines.
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Normalizing flows for all-orders QED corrections in lattice field theory
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.
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Diffusion model for SU(N) gauge theories
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.
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Enhanced Sampling Techniques for Lattice Gauge Theory
Metadynamics bias potentials and volume-extrapolation strategies reduce integrated autocorrelation times of topological charge in lattice gauge theories.
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Lattice field theories with a sign problem
Reviews approaches such as Lefschetz thimbles, complex Langevin dynamics, dual variables, tensor renormalization group, and machine learning to control the sign problem in lattice field theories.