PODiff performs conditional diffusion in a fixed, variance-ordered POD latent space to enable efficient probabilistic super-resolution of high-dimensional scientific fields with lower memory and better-calibrated uncertainty than pixel-space or dropout baselines.
(2024) Generative diffusion-based downscaling for climate
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Patch-PODiff-ViT defines a structured latent space via patchwise POD for efficient diffusion-based super-resolution and direct analytic uncertainty quantification across scientific and natural images.
Nano-TO with multi-shell filtering and conditional diffusion models co-optimizes topology and surface physics in aluminum nanostructures up to 650k atoms, revealing size-dependent truss-to-wall topology selection.
citing papers explorer
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PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
PODiff performs conditional diffusion in a fixed, variance-ordered POD latent space to enable efficient probabilistic super-resolution of high-dimensional scientific fields with lower memory and better-calibrated uncertainty than pixel-space or dropout baselines.
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Patch-PODiff-ViT: Structured Latent Diffusion with Patchwise POD for Super-Resolution and Uncertainty Quantification
Patch-PODiff-ViT defines a structured latent space via patchwise POD for efficient diffusion-based super-resolution and direct analytic uncertainty quantification across scientific and natural images.
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IPSL-AID: Generative Diffusion Models for Climate Downscaling from Global to Regional Scales
Nano-TO with multi-shell filtering and conditional diffusion models co-optimizes topology and surface physics in aluminum nanostructures up to 650k atoms, revealing size-dependent truss-to-wall topology selection.