⚛️ Physics & Scientific Computing¶

📷 CVPR2026 · 2 paper notes

AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts: AviaSafe embeds the "localization before quantification" hierarchical strategy and the long-validated "Icing Condition (IC) index" into a Swin Transformer backbone. It achieves the first global, 6-hourly, phase-separable (ice/liquid/rain/snow) cloud microphysics forecast, outperforming the FuXi baseline on 93.7% of variable-lead time combinations and matching or exceeding the operational NWP ECMWF HRES on key background variables up to a 7-day lead time.
Spatial-Spectral Residuals Informed Diffusion Neural Operator for Pan-sharpening: SRINO replaces the attention-based denoising backbone of diffusion models for pan-sharpening with a Galerkin-type Neural Operator (transferring the generation process to a continuous function space to significantly save FLOPs and memory). It treats pixel-level spatial/spectral consistency residuals directly as conditions fed into each step of the reverse sampling process for closed-loop guidance. On WV3/GF2/QB datasets, it outperforms current SOTA methods while being several times more computationally efficient than attention-based diffusion models.