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 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.
The study analyzes temperature dependence of Lee-Yang zeros and edge singularities in a finite-volume mean-field QCD model and compares finite-size scaling methods for identifying the critical point.
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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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Lee-Yang zeros and edge singularity in a mean-field approach
The study analyzes temperature dependence of Lee-Yang zeros and edge singularities in a finite-volume mean-field QCD model and compares finite-size scaling methods for identifying the critical point.