mlsanityData to 7 Oct 2026

Applications of Machine Learning to Lattice Quantum Field Theory

arXiv 2202.05838 ↗First posted 10 Feb 2022hep-lat cs.LG hep-phcited by 14

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Abstract (arXiv)

There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process, we discuss the unique requirements of machine learning for lattice quantum field theory research and outline what is needed to enable exploration and deployment of this approach in the future.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 34 of its 124 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

34 of 124 references.

References · 34

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  5. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
  6. 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
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22 more
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  3. 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
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Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 5.

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  1. 2023Diffusion Models as Stochastic Quantization in Lattice Field Theorycited by 24
  2. 2023Sampling the lattice Nambu-Goto string using Continuous Normalizing Flowscited by 12
  3. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  4. 2024Bayesian RG Flow in Neural Network Field Theoriescited by 9
  5. 2025Physics-Driven Learning for Inverse Problems in Quantum Chromodynamicscited by 7
  6. 2023Geometrical aspects of lattice gauge equivariant convolutional neural networkscited by 6
  7. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
  8. 2023Constructing and Machine Learning Calabi-Yau Five-foldscited by 4
  9. 2025Group-Equivariant Diffusion Models for Lattice Field Theorycited by 3
  10. 2024Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networkscited by 1
  11. 2025Phase diagram and eigenvalue dynamics of stochastic gradient descent in multilayer neural networkscited by 1
  12. 2024Neural Networks Asymptotic Behaviours for the Resolution of Inverse Problemscited by 0
2 more
  1. 2025Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIFcited by 0
  2. 2025Particle Monte Carlo methods for Lattice Field Theorycited by 0