Super-Resolution Reconstruction of Turbulence via Spatio-Frequency Distillation and Physics-Guided Learning

High-resolution turbulence data are essential for marine environment monitoring, climate change modeling, and aerospace engineering. However, numerical simulations and experimental measurements are often limited by high computational costs, insufficient sensor resolution, and sparse data acquisition, making it difficult to fully capture the multi scale details of turbulence. To address this challenge, a spatio-frequency fusion distillation network (SFDN) is developed to reconstruct finescale turbulent structures from low resolution inputs. The proposed model is built upon stacked Mamba-frequency distillation blocks, where each block integrates multiple Mamba-frequency fusion blocks (MFFBs) to refine and fuse the deep features of turbulence. By combining a Mamba-based state space model with a frequency attention mechanism, the MFFB is capable of capturing both long-range spatial dependencies and frequency-domain representations. Additionally, a physics-guided loss function is introduced to constrain the solution space and guide the learning process. To evaluate the performance of the proposed SFDN, comprehensive experiments were conducted on datasets of the forced isotropic turbulence and the turbulent channel flow, and comparisons were made with bicubic interpolation and several deep learning super-resolution models. The results demonstrate that SFDN achieves comparable or superior performance to state-of-the-art methods in terms of visual quality, quantitative accuracy, and preservation of physical characteristics, while using only 35.38% of the parameters of leading models. These findings highlight the effectiveness and efficiency of the proposed approach, as well as its strong generalization capability for reconstructing complex turbulent flows.