Physics field super-resolution reconstruction via enhanced diffusion model and fourier neural operator - Yanan Guo; Junqiang Song; Xiaoqun Cao; Chuanfeng Zhao; Hongze Leng (Theoretical and Applied Mechanics Letters)
With the growing demand for high-precision flow field simulations in computational science and engineering, the super-resolution reconstruction of physical fields has attracted considerable research interest. However, traditional numerical methods often entail high computational costs, involve complex data processing, and struggle to capture fine-scale high-frequency details. To address these challenges, we propose an innovative super-resolution reconstruction framework that integrates a Fourier neural operator (FNO) with an enhanced diffusion model. The framework employs an adaptively weighted FNO to process low-resolution flow field inputs, effectively capturing global dependencies and high-frequency features. Furthermore, a residual-guided diffusion model is introduced to further improve reconstruction performance. This model uses a Markov chain for noise injection in physical fields and integrates a reverse denoising procedure, efficiently solved by an adaptive time-step ordinary differential equation solver, thereby ensuring both stability and computational efficiency. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in terms of accuracy and efficiency, offering a promising solution for fine-grained data reconstruction in scientific simulations.