Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
We develop a methodology based on out-of-equilibrium simulations to mitigate topological freezing when approaching the continuum limit of lattice gauge theories. We reduce the autocorrelation of the topological charge employing open boundary conditions, while removing exactly their unphysical effects using a non-equilibrium Monte Carlo approach in which periodic boundary conditions are gradually switched on. We perform a detailed analysis of the computational costs of this strategy in the case of the four-dimensional $\mathrm{SU}(3)$ Yang-Mills theory. After achieving full control of the scaling, we outline a clear strategy to sample topology efficiently in the continuum limit, which we check at lattice spacings as small as $0.045$ fm. We also generalize this approach by designing a customized Stochastic Normalizing Flow for evolutions in the boundary conditions, obtaining superior performances with respect to the purely stochastic non-equilibrium approach, and paving the way for more efficient future flow-based solutions.
citation-role summary
background 1
citation-polarity summary
fields
hep-lat 4years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
unclear 1representative citing papers
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.
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 pedagogical review summarizing analytic predictions and recent lattice results for theta-dependence and topological susceptibility in QCD.
citing papers explorer
-
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.
-
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.
-
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.
-
Topological Susceptibility and QCD at Finite Theta Angle
A pedagogical review summarizing analytic predictions and recent lattice results for theta-dependence and topological susceptibility in QCD.