Neural networks parametrize gauge-equivariant trial states for Wilson loops and automatically yield interpolators for ground and excited states in quenched lattice QCD.
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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.
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
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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.
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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.
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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.
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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.
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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.