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.
arXiv preprint arXiv:2410.05431 , year=
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A diffusion-contrastive GNN with virtual nodes reduces wind nowcasting MAE by 30-46% in unobserved regions on Netherlands station data compared to interpolation and regression baselines.
SwAIther-Precip uses lead-time-conditioned U-Net bias correction followed by diffusion-based generative downscaling to reduce CRPS by 48% and achieve ~4 km effective resolution from 0.25° AIFS forecasts.
DiffUNet^2 is a bidirectional conditional diffusion model integrated with visual tools for probabilistic exploration of scientific time series across five evaluated datasets.
Learned PDE solving should target transport over admissible futures via flow learners, not snapshot state regression.
A review of Earth science foundation models covering capability evolution from perception to discovery, applications across atmosphere/hydrosphere/lithosphere/biosphere/anthroposphere/cryosphere, over 200 datasets, and key challenges.
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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A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions
A diffusion-contrastive GNN with virtual nodes reduces wind nowcasting MAE by 30-46% in unobserved regions on Netherlands station data compared to interpolation and regression baselines.
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SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
SwAIther-Precip uses lead-time-conditioned U-Net bias correction followed by diffusion-based generative downscaling to reduce CRPS by 48% and achieve ~4 km effective resolution from 0.25° AIFS forecasts.
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DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data
DiffUNet^2 is a bidirectional conditional diffusion model integrated with visual tools for probabilistic exploration of scientific time series across five evaluated datasets.
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Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing
Learned PDE solving should target transport over admissible futures via flow learners, not snapshot state regression.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
A review of Earth science foundation models covering capability evolution from perception to discovery, applications across atmosphere/hydrosphere/lithosphere/biosphere/anthroposphere/cryosphere, over 200 datasets, and key challenges.