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arXiv · 2312.15029

Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery

Mohammed Sardar·Alex Skillen·Małgorzata J. Zimoń·Samuel Draycott·Alistair Revell
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Abstract

We investigate the statistical recovery of missing physics and turbulent phenomena in fluid flows using generative machine learning. Here we develop a two-stage super-resolution method using spectral filtering to restore the high-wavenumber components of a Kolmogorov flow. We include a rigorous examination of generated samples through the lens of statistical turbulence. By extending the prior methods to a combined super-resolution and conditional high-wavenumber generation, we demonstrate turbulence recovery on a 8x upsampling task, effectively doubling the range of recovered wavenumbers.

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Mohammed Sardar, Alex Skillen, Małgorzata J. Zimoń, Samuel Draycott, Alistair Revell. 2023-12-22. Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery. https://doi.org/10.1063/5.0231664

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