Localization-constrained sparse quantum dictionaries trained on baker-map eigenstates spontaneously recover scar-like atoms aligned with classical periodic orbits.
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A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
A Temporal U-Net with perceptual loss and a physics-informed parabolic bridge interpolates sparse fluid observations, cutting MAE to 0.015 from 0.085 while retaining high-frequency turbulent structures.
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Unveiling Semiclassical Structures in Quantum Chaotic Eigenstates Using Neural Networks
Localization-constrained sparse quantum dictionaries trained on baker-map eigenstates spontaneously recover scar-like atoms aligned with classical periodic orbits.
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Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC
A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
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Physics-Informed Temporal U-Net for High-Fidelity Fluid Interpolation
A Temporal U-Net with perceptual loss and a physics-informed parabolic bridge interpolates sparse fluid observations, cutting MAE to 0.015 from 0.085 while retaining high-frequency turbulent structures.