[2603.00149] Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00149 (cs) [Submitted on 25 Feb 2026] Title:Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction Authors:Zhihao Li, Shengwei Dong, Chuang Yi, Junxuan Gao, Zhilu Lai, Zhiqiang Liu, Wei Wang, Guangtao Zhang View a PDF of the paper titled Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction, by Zhihao Li and 7 other authors View PDF Abstract:Existing image SR and generic diffusion models transfer poorly to fluid SR: they are sampling-intensive, ignore physical constraints, and often yield spectral mismatch and spurious divergence. We address fluid super-resolution (SR) with \textbf{ReMD} (\underline{Re}sidual-\underline{M}ultigrid \underline{D}iffusion), a physics-consistent diffusion framework. At each reverse step, ReMD performs a \emph{multigrid residual correction}: the update direction is obtained by coupling data consistency with lightweight physics cues and then correcting the residual across scales; the multiscale hierarchy is instantiated with a \emph{multi-wavelet} basis to capture both large structures and fine vortical details. This coarse-to-fine design accelerates convergence and preserves fine structures while remaining equation-free. Across atmospheric and oceanic benchmarks, ReMD improves accuracy and spectral fidelity, reduces divergence, and reaches comparable quality with markedly fewer sampli...