We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional Denoising Diffusion Probabilistic Models (DDPMs). The developed method is presented as a proof-of-concept generative surrogate for reconstructing coherent turbulent dynamics between sparse snapshots, demonstrated on a 2D Kolmogorov Flow, and a 3D Kelvin-Helmholtz Instability (KHI). We analyse the generated flow sequences through the lens of statistical turbulence, examining the time-averaged turbulent kinetic energy spectra over generated sequences, and temporal decay of turbulent structures. For the non-stationary Kelvin-Helmholtz Instability, we assess the ability of the proposed method to capture evolving flow statistics across the most strongly time-varying flow regime. We additionally examine instantaneous fields and physically motivated metrics at key stages of the KHI flow evolution.

翻译：本研究采用条件去噪扩散概率模型（DDPMs），探究了在湍流场稀疏且去相关的快照之间进行时间插值的时空流动序列的统计精度。所提出的方法作为一种概念验证的生成式代理模型，用于重建稀疏快照间连贯的湍流动力学，并在二维Kolmogorov流和三维Kelvin-Helmholtz不稳定性（KHI）上进行了验证。我们通过统计湍流的视角分析生成的流动序列，考察生成序列的时间平均湍流动能谱以及湍流结构的时间衰减特性。对于非平稳的Kelvin-Helmholtz不稳定性，我们评估了所提方法在流动变化最剧烈的时间段内捕捉演化流场统计量的能力。此外，我们还检查了KHI流动演化关键阶段的瞬时场及基于物理的度量指标。