Conditional MAFs interpolate QCD chiral phase structure across coupling, mass, and volume, reproducing reweighting while cutting required ensembles despite bias near transitions.
Lawrence,Machine-learning approaches to accelerating lattice simulations,PoS LATTICE2024(2025) 010 [2502.02670]
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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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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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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.