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.
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Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.
citing papers explorer
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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.
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Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Wasserstein GAN super-resolution recovers near-wall velocities in 4D Flow MRI with vNRMSE 6.9% versus 9.6% for non-adversarial baseline, though training stability depends on loss function choice.