Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery

Understand

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.