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Gauge covariant neural network for quarks and gluons
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We propose gauge-covariant neural networks along with a specialized training algorithm for lattice QCD, designed to handle realistic quarks and gluons in four-dimensional space-time. We show that the smearing procedure can be interpreted as an extended version of residual neural networks with fixed parameters. To demonstrate the applicability of our neural networks, we develop a self-learning hybrid Monte Carlo algorithm in the context of two-color QCD, yielding outcomes consistent with those from the conventional Hybrid Monte Carlo approach.
Forward citations
Cited by 2 Pith papers
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A Machine Learning Approach for Lattice Gauge Fixing
A Wilson-line-bundle CNN generates approximate Coulomb-gauge transformations that, combined with iterative gauge fixing, reduce lattice-gauge-fixing cost by ~1–4% and transfer across lattice volumes.
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Symmetry-preserving neural networks in lattice field theories
Translation- and gauge-equivariant neural networks (L-CNNs) predict Wilson loops, topological charge, and flux observables with orders-of-magnitude lower error than symmetry-breaking baselines, and neural gradient flo...
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