Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908741460295680 |
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| author | Sardar, Mohammed Zimoń, Małgorzata J. Draycott, Samuel Revell, Alistair Skillen, Alex |
| author_facet | Sardar, Mohammed Zimoń, Małgorzata J. Draycott, Samuel Revell, Alistair Skillen, Alex |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models Sardar, Mohammed Zimoń, Małgorzata J. Draycott, Samuel Revell, Alistair Skillen, Alex Fluid Dynamics Machine Learning 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. |
| title | Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2512.24813 |