mlsanityData to 14 Oct 2026

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

arXiv 2503.11482 ↗First posted 14 Mar 2025hep-lat cs.LGcited by 1

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Abstract (arXiv)

We present the textttNeuMC 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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 40 references that are in the map. First posted on arXiv 14 Mar 2025.

Builds on

25 of 40 references.

References · 25

  1. 2016Density estimation using Real NVPcited by 2,042
  2. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  3. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 148
  4. 2020Normalizing Flows on Tori and Spherescited by 117
  5. 2020Equivariant flow-based sampling for lattice gauge theorycited by 103
  6. 2018Solving Statistical Mechanics Using Variational Autoregressive Networkscited by 100
  7. 2016Variational inference for Monte Carlo objectivescited by 84
  8. 2023normflows: A PyTorch Package for Normalizing Flowscited by 56
  9. 2022Learning Lattice Quantum Field Theories with Equivariant Continuous Flowscited by 37
  10. 2021Introduction to Normalizing Flows for Lattice Field Theorycited by 35
  11. 2022Gauge-equivariant flow models for sampling in lattice field theories with pseudofermionscited by 31
  12. 2021Flow-based sampling for fermionic lattice field theoriescited by 27
13 more
  1. 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
  2. 2022Stochastic normalizing flows as non-equilibrium transformationscited by 25
  3. 2023Diffusion Models as Stochastic Quantization in Lattice Field Theorycited by 24
  4. 2022Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flowscited by 23
  5. 2023Normalizing flows for lattice gauge theory in arbitrary space-time dimensioncited by 23
  6. 2022Flow-based sampling in the lattice Schwinger model at criticalitycited by 20
  7. 2021Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulationscited by 10
  8. 2024Applications of flow models to the generation of correlated lattice QCD ensemblescited by 10
  9. 2024Scaling of Stochastic Normalizing Flows in SU(3) lattice gauge theorycited by 10
  10. 2025Physics-Conditioned Diffusion Models for Lattice Gauge Theorycited by 9
  11. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
  12. 2024Non-Perturbative Trivializing Flows for Lattice Gauge Theoriescited by 3
  13. 2023Training normalizing flows with computationally intensive target probability distributionscited by 2

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Citing papers per year: 2025: 1.

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  1. 2025Particle Monte Carlo methods for Lattice Field Theorycited by 0