PINNs optimize Dirac operators to satisfy the Ginsparg-Wilson relation, reproducing overlap fermions and autonomously recovering both the standard and a Fujikawa-type generalized GW relation via polynomial ansatz search.
Machine learning a fixed point action for SU(3) 20 gauge theory with a gauge equivariant convolutional neural network,
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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.
The FLAG 2024 review provides updated averages of lattice QCD determinations for quark masses, decay constants, form factors, mixing parameters, and nucleon matrix elements.
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Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions
PINNs optimize Dirac operators to satisfy the Ginsparg-Wilson relation, reproducing overlap fermions and autonomously recovering both the standard and a Fujikawa-type generalized GW relation via polynomial ansatz search.
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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.
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FLAG Review 2024
The FLAG 2024 review provides updated averages of lattice QCD determinations for quark masses, decay constants, form factors, mixing parameters, and nucleon matrix elements.