FOT-CFM generates turbulent fields in function space with superior high-order statistics and energy spectra on Navier-Stokes, Kolmogorov flow, and Hasegawa-Wakatani equations compared to baselines.
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A conditional diffusion model super-resolves coarse ABL LES data, recovering fine turbulent structures and Reynolds stresses accurately inside the training distribution but producing noise and over-predicted stresses when wind speeds are extrapolated.
CGSoRec denoises social relations and reweights user social preferences to serve as conditions that steer a diffusion recommender away from popularity bias.
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Conditional diffusion denoising probabilistic model for super-resolution of atmospheric boundary layer large eddy simulation
A conditional diffusion model super-resolves coarse ABL LES data, recovering fine turbulent structures and Reynolds stresses accurately inside the training distribution but producing noise and over-predicted stresses when wind speeds are extrapolated.