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-Driven Learning for Inverse Problems in Quantum Chromodynamics
6 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from complex data sets. This is particularly relevant for quantum chromodynamics (QCD), the theory of strong interactions, with its inherent limitations in observational data and demanding computational approaches. This perspective highlights advances and potential of physics-driven learning methods, focusing on predictions of physical quantities towards QCD physics, and drawing connections to machine learning(ML). It is shown that the fusion of ML and physics can lead to more efficient and reliable problem-solving strategies. Key ideas of ML, methodology of embedding physics priors, and generative models as inverse modelling of physical probability distributions are introduced. Specific applications cover first-principle lattice calculations, and QCD physics of hadrons, neutron stars, and heavy-ion collisions. These examples provide a structured and concise overview of how incorporating prior knowledge such as symmetry, continuity and equations into deep learning designs can address diverse inverse problems across different physical sciences.
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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.
Fully convolutional diffusion models trained on small lattices transfer to unseen larger volumes for 2D/3D phi^4 sampling across phases, matching or beating same-size training on most observables.
Finite current relaxation introduces memory effects that suppress, shift, and reshape non-monotonic cumulant behavior relative to instantaneous equilibrium and Fickian diffusion, most visibly in higher-order cumulants.
SVD truncation of the exp(-ωt) kernel reconstructs smeared spectral functions from lattice correlators with controlled uncertainties and approaches the Mellin transform in the continuum limit.
A sampling-based extension of Nevanlinna–Pick interpolation propagates lattice uncertainties into an error estimate that shrinks as more Euclidean data points are added.
citing papers explorer
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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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Diffusion Models for Sampling Near Criticality in Lattice Field Theories
Fully convolutional diffusion models trained on small lattices transfer to unseen larger volumes for 2D/3D phi^4 sampling across phases, matching or beating same-size training on most observables.
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Cumulant dynamics in finite-memory diffusion
Finite current relaxation introduces memory effects that suppress, shift, and reshape non-monotonic cumulant behavior relative to instantaneous equilibrium and Fickian diffusion, most visibly in higher-order cumulants.
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Spectral reconstruction from Euclidean lattice correlators through singular value decomposition
SVD truncation of the exp(-ωt) kernel reconstructs smeared spectral functions from lattice correlators with controlled uncertainties and approaches the Mellin transform in the continuum limit.
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Nevanlinna-Pick interpolation from uncertain data
A sampling-based extension of Nevanlinna–Pick interpolation propagates lattice uncertainties into an error estimate that shrinks as more Euclidean data points are added.