Tuesday, January 5, 2021

Machine Learning for Fluid Dynamics: Patterns


The data set he mentions is Johns Hopkins Turbulence Databases. Many hundreds of Terabytes of direct numerical simulations with different governing equations and boundary conditions.


  1. Machine Learning for Fluid Mechanics
    Steven Brunton, Bernd Noack, Petros Koumoutsakos

    The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.