A novel gauge-equivariant neural-network preconditioner for the Dirac equation in lattice QCD mitigates critical slowing down and transfers to unseen configurations without retraining.
Introduction to Normalizing Flows for Lattice Field Theory
6 Pith papers cite this work, alongside 15 external citations. Polarity classification is still indexing.
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Pith papers citing it
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abstract
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The ideas and approaches proposed in arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a concrete implementation of the framework is presented. We apply this framework to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding gauge symmetries in the flow-based approach to the latter. This presentation is intended to be interactive and working with the attached Jupyter notebook is recommended.
years
2026 6representative citing papers
A JEPA-based hypernetwork maps lattice field theory couplings to flow-model weights, and the geometry of those weights recovers the phase transition, intrinsic dimension, and Ising critical exponent of 2D scalar field theory without supervised physics labels.
A coupling-flow global proposal for Monte Carlo sampling in 2D pure SU(2) lattice gauge theory is shown to be formally valid and to reproduce the target ensemble in proof-of-principle tests, with modest hybrid gains but no clear outperformance over local baselines.
Fully convolutional diffusion models trained on small lattices transfer to unseen larger volumes for 2D/3D phi^4 sampling across phases, matching or beating same-size training on most observables.
SURF uses teacher-student remixing within a flow-matching framework to achieve unsupervised source separation and reports new state-of-the-art results on audio and image benchmarks.
citing papers explorer
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A novel gauge-equivariant neural-network architecture for preconditioners in lattice QCD
A novel gauge-equivariant neural-network preconditioner for the Dirac equation in lattice QCD mitigates critical slowing down and transfers to unseen configurations without retraining.
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Weight-Space Physics: Interpretable Hypernetworks for Lattice Quantum Field Theories
A JEPA-based hypernetwork maps lattice field theory couplings to flow-model weights, and the geometry of those weights recovers the phase transition, intrinsic dimension, and Ising critical exponent of 2D scalar field theory without supervised physics labels.
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Flow-Based Global Proposals for Monte Carlo Sampling in SU(2) Lattice Gauge Theory
A coupling-flow global proposal for Monte Carlo sampling in 2D pure SU(2) lattice gauge theory is shown to be formally valid and to reproduce the target ensemble in proof-of-principle tests, with modest hybrid gains but no clear outperformance over local baselines.
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Diffusion Models for Sampling Near Criticality in Lattice Field Theories
Fully convolutional diffusion models trained on small lattices transfer to unseen larger volumes for 2D/3D phi^4 sampling across phases, matching or beating same-size training on most observables.
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SURF: Separation via Unsupervised Remixing Flow
SURF uses teacher-student remixing within a flow-matching framework to achieve unsupervised source separation and reports new state-of-the-art results on audio and image benchmarks.
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