NeuMC -- a package for neural sampling for lattice field theories

Abstract

We present the \texttt{NeuMC} software package, based on \pytorch, aimed at facilitating the research on neural samplers in lattice field theories. Neural samplers based on normalizing flows are becoming increasingly popular in the context of Monte-Carlo simulations as they can effectively approximate target probability distributions, possibly alleviating some shortcomings of the Markov chain Monte-Carlo methods. Our package provides tools to create such samplers for two-dimensional field theories.

42 pages, 15 figures, for associated code repository, see https://github.com/nmcmc/neumc.git

References in corpus (12)

• Normalizing Flows: An Introduction and Review of Current Methods
• Solving Statistical Mechanics Using Variational Autoregressive Networks
• Flow-based generative models for Markov chain Monte Carlo in lattice field theory
• Equivariant flow-based sampling for lattice gauge theory
• Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
• Asymptotically unbiased estimation of physical observables with neural samplers
• Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
• normflows: A PyTorch Package for Normalizing Flows
• Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
• Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulations
• Training normalizing flows with computationally intensive target probability distributions
• Rényi entanglement entropy of spin chain with Generative Neural Networks