Neural networks parametrize gauge-equivariant trial states for Wilson loops and automatically yield interpolators for ground and excited states in quenched lattice QCD.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
fields
hep-lat 7representative citing papers
Neural networks parametrize gauge-invariant interpolators that extract ground-state Wilson loops with improved signal-to-noise ratio compared to traditional methods while preserving gauge invariance.
A neural network trained on 2D SU(2) lattices with inserted thin Z2 vortices, after random gauge transformations, noise, and cooling, can locate center vortices at moderate visibility levels and scales via tiling.
Implicit score matching trains diffusion models that successfully sample SU(3) Wilson gauge configurations on lattices, with a Hamiltonian-dynamics corrector needed for strong coupling.
Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.
citing papers explorer
-
Neural network interpolators for Wilson loops
Neural networks parametrize gauge-equivariant trial states for Wilson loops and automatically yield interpolators for ground and excited states in quenched lattice QCD.
-
Wilson loops with neural networks
Neural networks parametrize gauge-invariant interpolators that extract ground-state Wilson loops with improved signal-to-noise ratio compared to traditional methods while preserving gauge invariance.
-
First steps towards gauge-independent vortex identification through machine learning
A neural network trained on 2D SU(2) lattices with inserted thin Z2 vortices, after random gauge transformations, noise, and cooling, can locate center vortices at moderate visibility levels and scales via tiling.
-
Diffusion model for SU(N) gauge theories
Implicit score matching trains diffusion models that successfully sample SU(3) Wilson gauge configurations on lattices, with a Hamiltonian-dynamics corrector needed for strong coupling.
-
Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory
Out-of-equilibrium simulations with open-to-periodic boundary switching plus a tailored stochastic normalizing flow enable efficient topology sampling in the continuum limit of four-dimensional SU(3) Yang-Mills theory.
-
Machine learning for four-dimensional SU(3) lattice gauge theories
Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.
- Higher-order hopping-parameter expansion by human-AI collaboration