Concerning the Use of Turbulent Flow Data for Machine Learning

Autor:	Sardar, Mohammed, Zimoń, Małgorzata J., Draycott, Samuel, Revell, Alistair, Skillen, Alex
Rok vydání:	2024
Předmět:	Physics - Fluid Dynamics
Druh dokumentu:	Working Paper
Popis:	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) the requirements for a fair validation set, and 2) the pitfalls in downsampling DNS data before training. We attempt to shed light on the impact these issues can have on machine learning and computer vision for turbulence. Further, we include statistical and spectral analysis for the homogenous isotropic turbulence from the John Hopkins Turbulence Database, a Kolmogorov flow, and a Rayleigh-B\'enard Convection Cell using data generated by the authors, to concretely demonstrate these issues. Comment: 10 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.06050 Zobrazit plný text záznamu View this record from Arxiv