11 citations · 12 across the 3 of their papers we have counts for
3 papers
Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
Mohammed Sardar, Małgorzata J. Zimoń, Samuel Draycott +2
We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional D…
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)…
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…