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Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

arXiv 2107.00734 ↗First posted 1 Jul 2021hep-lat cond-mat.stat-mech cs.LGcited by 25

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Abstract (arXiv)

Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e.~vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate different flow-based sampling algorithms, including a composite sampling algorithm where flow-based proposals are occasionally augmented by applying updates using traditional algorithms like HMC.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 0 of its 15 references that are in the map. First posted on arXiv 1 Jul 2021.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 9; 2024: 3; 2025: 1; 2026: 1.

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  1. 2022Stochastic normalizing flows as non-equilibrium transformationscited by 25
  2. 2021Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flowscited by 24
  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. 2022Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flowscited by 22
  6. 2021Local-Global MCMC kernels: the best of both worldscited by 21
  7. 2023On Sampling with Approximate Transport Mapscited by 20
  8. 2022Applications of Machine Learning to Lattice Quantum Field Theorycited by 14
  9. 2023Generative Diffusion Models for Lattice Field Theorycited by 13
  10. 2023Sampling the lattice Nambu-Goto string using Continuous Normalizing Flowscited by 12
  11. 2021Analysis of autocorrelation times in Neural Markov Chain Monte Carlo simulationscited by 10
  12. 2021Machine Learning of Thermodynamic Observables in the Presence of Mode Collapsecited by 10
13 more
  1. 2024AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learningcited by 6
  2. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
  3. 2021Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Upcited by 5
  4. 2022Simulating first-order phase transition with hierarchical autoregressive networkscited by 5
  5. 2021Machine Learning in Nuclear Physicscited by 4
  6. 2023Balanced Training of Energy-Based Models with Adaptive Flow Samplingcited by 4
  7. 2023Learning to Sample Bettercited by 4
  8. 2023The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flowscited by 2
  9. 2022Numerical and geometrical aspects of flow-based variational quantum Monte Carlocited by 1
  10. 2025NeuMC -- a package for neural sampling for lattice field theoriescited by 1
  11. 2023Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulationscited by 0
  12. 2024An AI-powered Technology Stack for Solving Many-Electron Field Theorycited by 0
  13. 2026Operator-Guided Model Reduction for Generative Sampling in Lattice Field Theorycited by 0