Citing Articles For: Reconstructing spectral functions via automatic differentiation

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Articles Citing

Reconstructing spectral functions via automatic differentiation

Noiseless limit and improved-prior limit of the maximum entropy method and their implications for the analytic continuation problem

Nucleon axial-vector form factor and radius from radiatively corrected antineutrino scattering data

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High-Throughput full-spectrum characterization of semiconductor thin films via AD-accelerated physics-informed inversion: Demonstrations in Si and SiC epitaxy

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Toward constraining QCD phase transitions in neutron star interiors: Bayesian inference with a Tolman-Oppenheimer-Volkof linear response analysis

Reconstruction of partially obscured objects with a physics-driven self-training neural network

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Deep learning for exploring hadron–hadron interactions

Ill-posedness in limited discrete Fourier inversion and regularization for quasi distributions in LaMET

Neural network modeling of heavy-quark potential from holography

Hybrid multi-head physics-informed neural network for depth estimation in terahertz imaging

Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions

Exploring QCD matter in extreme conditions with Machine Learning

Reconstructing lattice QCD spectral functions with stochastic pole expansion and Nevanlinna analytic continuation

Hadronic vacuum polarization: Comparing lattice QCD and data-driven results in systematically improvable ways

Studying high-energy nuclear physics with machine learning

Amplitude/Phase Retrieval for Terahertz Holography With Supervised and Unsupervised Physics-Informed Deep Learning

Teaching to extract spectral densities from lattice correlators to a broad audience of learning-machines

No title available

Hadronic structure, conformal maps, and analytic continuation

Reconstructing the neutron star equation of state from observational data via automatic differentiation

Examination of nucleon distribution with Bayesian imaging for isobar collisions

High-energy nuclear physics meets machine learning

No title available

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