1 citations · 1 across the 2 of their papers we have counts for
3 papers
Concerning the Use of Turbulent Flow Data for Machine Learning
Mohammed Sardar, Małgorzata J. Zimoń, 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)…
Reducing data resolution for better super-resolution: Reconstructing turbulent flows from noisy observation
Kyongmin Yeo, Małgorzata J. Zimoń, 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…
Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery
Mohammed Sardar, Alex Skillen, Małgorzata J. Zimoń +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…