activity
20242026
collaborators

5 papers

physics.flu-dyn2026

Scale by scale analysis of magnetoconvection with uniform wall-normal and wall-parallel magnetic fields at low magnetic Reynolds number

Jake Ineson, Aleksander Dubas, Alex Skillen

Rayleigh-Bénard convection under an imposed inductionless magnetic field is analysed statistically from the perspective of single-point and multi-scale energy budgets. The data is…

physics.plasm-ph2026

Unstable magnetic reconnection self-generates turbulence

Nick Williams, Alessandro De Rosis, Alex Skillen

Magnetic reconnection and turbulence are deeply intertwined in magnetohydrodynamic flows, yet how reconnection self-generates turbulence remains unclear. Using an ensemble of high-…

physics.flu-dyn2025

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…

physics.flu-dyn2025

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…

physics.flu-dyn2024

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)…