mlsanityData to 14 Oct 2026

Practical applications of machine-learned flows on gauge fields

arXiv 2404.11674 ↗First posted 17 Apr 2024hep-lat cond-mat.stat-mech cs.LGcited by 6

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Abstract (arXiv)

Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question how flows can improve lattice QCD at state-of-the-art scales. We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling, aimed at improving topological mixing, which are viable with iterative improvements upon presently available flows.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 25 of its 74 references that are in the map. First posted on arXiv 17 Apr 2024.

Builds on

25 of 74 references.

References · 25

  1. 2015Variational Inference with Normalizing Flowscited by 2,170
  2. 2016Density estimation using Real NVPcited by 2,042
  3. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  4. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  5. 2020Stochastic Normalizing Flowscited by 157
  6. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 148
  7. 2018Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysiscited by 110
  8. 2020Equivariant flow-based sampling for lattice gauge theorycited by 103
  9. 2021Annealed Flow Transport Monte Carlocited by 90
  10. 2022Continual Repeated Annealed Flow Transport Monte Carlocited by 66
  11. 2018Neural Network Renormalization Groupcited by 48
  12. 2022Gauge-equivariant flow models for sampling in lattice field theories with pseudofermionscited by 31
13 more
  1. 2021Flow-based sampling for fermionic lattice field theoriescited by 27
  2. 2022Aspects of scaling and scalability for flow-based sampling of lattice QCDcited by 26
  3. 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
  4. 2022Stochastic normalizing flows as non-equilibrium transformationscited by 25
  5. 2022Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flowscited by 23
  6. 2023Normalizing flows for lattice gauge theory in arbitrary space-time dimensioncited by 23
  7. 2023Advances in machine-learning-based sampling motivated by lattice quantum chromodynamicscited by 23
  8. 2022Flow-based sampling in the lattice Schwinger model at criticalitycited by 20
  9. 2022Applications of Machine Learning to Lattice Quantum Field Theorycited by 14
  10. 2024Applications of flow models to the generation of correlated lattice QCD ensemblescited by 10
  11. 2021Deep Learning Hamiltonian Monte Carlocited by 7
  12. 2021HMC with Normalizing Flowscited by 6
  13. 2021LeapfrogLayers: A Trainable Framework for Effective Topological Samplingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2025Accelerated Parallel Tempering via Neural Transportscited by 17
  2. 2024Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defectscited by 13
  3. 2024Scaling of Stochastic Normalizing Flows in SU(3) lattice gauge theorycited by 10
  4. 2026Generative Models and Statistical Validationcited by 2
  5. 2025NeuMC -- a package for neural sampling for lattice field theoriescited by 1
  6. 2026Machine learning for four-dimensional SU(3) lattice gauge theoriescited by 0