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.
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.
Stanford flat plate boundary layer data set
ReplyDeleteMachine Learning for Fluid Mechanics
ReplyDeleteSteven Brunton, Bernd Noack, Petros Koumoutsakos
Abstract:
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.
Self-critical machine-learning wall-modeled LES for external aerodynamics.
ReplyDeleteA. Lozano-Durán and H. J. Bae