A gauge-equivariant diffusion model samples Schwinger model configurations, yielding unbiased observables matching MCMC and qualitatively less topological freezing than HMC.
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Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
A hierarchical generative model for critical lattice scalar field theories achieves orders-of-magnitude lower autocorrelation times than HMC while enabling exact multilevel Monte Carlo.
Operator projections of trained sampler functions in 2D phi^4 lattice theory decompose residuals into zero-mode Binder and finite-k correlator components, distinguishing flow-matching, diffusion, and normalizing-flow models.
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.
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.
Generative models learn conditional local distributions conditioned on neighbors and action parameters to improve Heatbath proposals for continuous-variable lattice models without target samples.
Trie-structured algorithms compute κ^8 to κ^12 terms in the hopping expansion of Tr ln M at costs scaling from 20x to 8900x a staple, verified by direct comparison to a reference calculation.
Metadynamics bias potentials and volume-extrapolation strategies reduce integrated autocorrelation times of topological charge in lattice gauge theories.
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.
A review of lattice QCD findings on the finite-temperature QCD transition at zero baryon chemical potential, its chiral limit behavior, constraints on the phase boundary and critical endpoint at finite density, plus advances under external fields and conditions.
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Sampling the Schwinger Model with Gauge-Equivariant Diffusion
A gauge-equivariant diffusion model samples Schwinger model configurations, yielding unbiased observables matching MCMC and qualitatively less topological freezing than HMC.
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Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition
Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
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Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality
A hierarchical generative model for critical lattice scalar field theories achieves orders-of-magnitude lower autocorrelation times than HMC while enabling exact multilevel Monte Carlo.
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Operator Spectroscopy of Trained Lattice Samplers
Operator projections of trained sampler functions in 2D phi^4 lattice theory decompose residuals into zero-mode Binder and finite-k correlator components, distinguishing flow-matching, diffusion, and normalizing-flow models.
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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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Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory
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.
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Higher-order hopping-parameter expansion by human-AI collaboration
Trie-structured algorithms compute κ^8 to κ^12 terms in the hopping expansion of Tr ln M at costs scaling from 20x to 8900x a staple, verified by direct comparison to a reference calculation.
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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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Machine learning for four-dimensional SU(3) lattice gauge theories
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.
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Lattice QCD at finite temperature and density
A review of lattice QCD findings on the finite-temperature QCD transition at zero baryon chemical potential, its chiral limit behavior, constraints on the phase boundary and critical endpoint at finite density, plus advances under external fields and conditions.