Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
A probabilistic denoising model recovers spectral features from Poisson-noisy 3D ARPES data at 0.02 electrons per voxel and propagates uncertainties into superconducting gap fits for cuprate superconductors.
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.
Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.
citing papers explorer
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Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
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Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.19 while recovering Pomeron intercept of 1.0786.
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Extraction of the color dipole amplitude with physics-informed neural networks
Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
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Probabilistic denoising for reliable signal extraction in spectroscopy
A probabilistic denoising model recovers spectral features from Poisson-noisy 3D ARPES data at 0.02 electrons per voxel and propagates uncertainties into superconducting gap fits for cuprate superconductors.
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Neural Network Representation of Generalized Parton Distributions (NNGPD)
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.
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Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.