mlsanityData to 7 Oct 2026

Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions

arXiv 2207.08945 ↗First posted 18 Jul 2022hep-lat cs.LGcited by 30

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Abstract (arXiv)

This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art lattice field theory calculations, making this development critical to the practical application of flow models to theories such as QCD. Methods by which flow-based sampling approaches can be improved via standard techniques such as even/odd preconditioning and the Hasenbusch factorization are also outlined. Numerical demonstrations in two-dimensional U(1) and SU(3) gauge theories with Nf=2 flavors of fermions are provided.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 0 of its 29 references that are in the map. First posted on arXiv 18 Jul 2022.

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Citing papers per year: 2022: 4; 2023: 12; 2024: 3; 2025: 6; 2026: 5.

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  1. 2023Swallowing the Bitter Pill: Simplified Scalable Conformer Generationcited by 55
  2. 2023Learning Interpolations between Boltzmann Densitiescited by 42
  3. 2022Aspects of scaling and scalability for flow-based sampling of lattice QCDcited by 26
  4. 2023Diffusion Models as Stochastic Quantization in Lattice Field Theorycited by 24
  5. 2023Advances in machine-learning-based sampling motivated by lattice quantum chromodynamicscited by 23
  6. 2023Neural Network Field Theories: Non-Gaussianity, Actions, and Localitycited by 21
  7. 2023On Sampling with Approximate Transport Mapscited by 20
  8. 2023Sampling the lattice Nambu-Goto string using Continuous Normalizing Flowscited by 12
  9. 2022Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulationcited by 11
  10. 2023Gauge-equivariant neural networks as preconditioners in lattice QCDcited by 8
  11. 2023Geometrical aspects of lattice gauge equivariant convolutional neural networkscited by 6
  12. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
18 more
  1. 2025Scaling flow-based approaches for topology sampling in SU(3) gauge theorycited by 6
  2. 2022Simulating first-order phase transition with hierarchical autoregressive networkscited by 5
  3. 2023Gauge-equivariant pooling layers for preconditioners in lattice QCDcited by 5
  4. 2023Mutual information of spin systems from autoregressive neural networkscited by 4
  5. 2025Group-Equivariant Diffusion Models for Lattice Field Theorycited by 3
  6. 2023Training normalizing flows with computationally intensive target probability distributionscited by 2
  7. 2025IsingFormer: Augmenting Parallel Tempering With Learned Proposalscited by 2
  8. 2026Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theoriescited by 2
  9. 2022Deformations of Boltzmann Distributionscited by 1
  10. 2024Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flowscited by 1
  11. 2025NeuMC -- a package for neural sampling for lattice field theoriescited by 1
  12. 2025Viability of perturbative expansion for quantum field theories on neuronscited by 1
  13. 2026A scalable flow-based approach to mitigate topological freezingcited by 1
  14. 2026Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theoriescited by 1
  15. 2024Stochastic normalizing flows for Effective String Theorycited by 0
  16. 2025Studying Effective String Theory using deep generative modelscited by 0
  17. 2026Machine learning for four-dimensional SU(3) lattice gauge theoriescited by 0
  18. 2026Sampling the Schwinger Model with Gauge-Equivariant Diffusioncited by 0