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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
A PINN-trained quasi-parton model reproduces lattice cumulants at vanishing chemical potentials and supplies a consistent four-dimensional QCD equation of state at finite densities.
citing papers explorer
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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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Unified Extraction of In-Medium Heavy Quark Potentials from RHIC to LHC Energies via Deep Learning
Deep learning extracts a unified in-medium heavy quark potential from multi-energy bottomonium data, finding the real part close to vacuum Cornell form with weak screening while the imaginary part dominates suppression.
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Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
A PINN-trained quasi-parton model reproduces lattice cumulants at vanishing chemical potentials and supplies a consistent four-dimensional QCD equation of state at finite densities.