Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery
Fluid Dynamics
2025-07-01 v2 Computational Physics
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.
Cite
@article{arxiv.2312.15029,
title = {Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery},
author = {Mohammed Sardar and Alex Skillen and Małgorzata J. Zimoń and Samuel Draycott and Alistair Revell},
journal= {arXiv preprint arXiv:2312.15029},
year = {2025}
}
Comments
28 pages, 15 figures