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Flow-based sampling in the lattice Schwinger model at criticality

arXiv 2202.11712 ↗First posted 23 Feb 2022hep-lat cond-mat.stat-mech cs.LGcited by 20

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Abstract (arXiv)

Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass. In contrast, at the same parameters, conventional methods fail to sample all parts of configuration space, leading to severely underestimated uncertainties.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 0 of its 23 references that are in the map. First posted on arXiv 23 Feb 2022.

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Citing papers per year: 2022: 4; 2023: 5; 2024: 4; 2025: 4; 2026: 3.

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  1. 2023Learning Interpolations between Boltzmann Densitiescited by 42
  2. 2022Aspects of scaling and scalability for flow-based sampling of lattice QCDcited by 26
  3. 2023Diffusion Models as Stochastic Quantization in Lattice Field Theorycited by 24
  4. 2023Advances in machine-learning-based sampling motivated by lattice quantum chromodynamicscited by 23
  5. 2022Hierarchical autoregressive neural networks for statistical systemscited by 12
  6. 2024Energy based diffusion generator for efficient sampling of Boltzmann distributionscited by 7
  7. 2024AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learningcited by 6
  8. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
  9. 2025Scaling flow-based approaches for topology sampling in SU(3) gauge theorycited by 6
  10. 2022Simulating first-order phase transition with hierarchical autoregressive networkscited by 5
  11. 2023Mutual information of spin systems from autoregressive neural networkscited by 4
  12. 2025Group-Equivariant Diffusion Models for Lattice Field Theorycited by 3
8 more
  1. 2023Training normalizing flows with computationally intensive target probability distributionscited by 2
  2. 2026Generative Models and Statistical Validationcited by 2
  3. 2022Deformations of Boltzmann Distributionscited by 1
  4. 2024Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flowscited by 1
  5. 2025NeuMC -- a package for neural sampling for lattice field theoriescited by 1
  6. 2026A scalable flow-based approach to mitigate topological freezingcited by 1
  7. 2025Particle Monte Carlo methods for Lattice Field Theorycited by 0
  8. 2026Sampling the Schwinger Model with Gauge-Equivariant Diffusioncited by 0