3 papers
physics.flu-dyn2025
Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery
Mohammed Sardar, Alex Skillen, MaÅgorzata J. ZimoÅ +2
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 me…
physics.flu-dyn2024
Concerning the Use of Turbulent Flow Data for Machine Learning
Mohammed Sardar, MaÅgorzata J. ZimoÅ, Samuel Draycott +2
This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1)…
physics.flu-dyn2024
Reducing data resolution for better super-resolution: Reconstructing turbulent flows from noisy observation
Kyongmin Yeo, MaÅgorzata J. ZimoÅ, Mykhaylo Zayats +1
A super-resolution (SR) method for the reconstruction of Navier-Stokes (NS) flows from noisy observations is presented. In the SR method, first the observation data is averaged ove…