用扩散模型从稀疏快照重建湍流动态，保持时间一致性。
Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
- 基于条件扩散模型，从稀疏快照生成连贯湍流序列。
- 重建的湍流动能谱与真实数据接近，结构衰减趋势一致。
- 适用于研究非定常湍流演化，如开尔文-赫姆霍兹不稳定性。
我们研究了利用条件去噪扩散概率模型（DDPM）在稀疏、非相关湍流快照之间进行时序插值的统计精度。该方法作为生成代理模型，用于重建稀疏快照间的相干湍流动力学，验证于二维柯尔莫哥洛夫流和三维开尔文-赫姆霍兹不稳定性（KHI）。通过统计湍流特性分析生成序列：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。针对非平稳的开尔文-赫姆霍兹不稳定性，评估模型在强时变流态中捕捉演化统计特性能力，并检查关键阶段的瞬时场及物理相关指标。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。