@inproceedings{0d364ef5761c4f608fff66cfd2e32cd8,
title = "Machine learning methods for development of data-driven turbulence models",
abstract = "The implementation of the machine learning methods of convolutional neural network combined with support vector machines to enhance RANS closure models is presented. The RANS models are not universal and accurate, however they are computationally affordable. Finding a way to improve the model predictability will be an advantage, and machine learning algorithms based on available high-fidelity data sets for canonical flow cases obtained from DNS and measurements can be helpful for this. The application of these algorithms for a fully-developed turbulent channel flow between parallel walls, with periodic hills and for other cases is considered.",
keywords = "SIMULATION, FLOW",
author = "Yakovenko, {Sergey N.} and Omid Razizadeh",
note = "Funding Information: The present study is partly supported by Russian Foundation for Basic Research (Project No. 17-01-00332) and performed within the framework of the Program of Fundamental Scientific Research of the state academies of sciences in 2013-2020 (Project No. ȺȺȺȺ-Ⱥ17-117030610128-8). Publisher Copyright: {\textcopyright} 2020 Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 27th Conference on High-Energy Processes in Condensed Matter, HEPCM 2020 ; Conference date: 29-06-2020 Through 03-07-2020",
year = "2020",
month = oct,
day = "26",
doi = "10.1063/5.0028572",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Fomin, {Vasily M.}",
booktitle = "High-Energy Processes in Condensed Matter, HEPCM 2020",
}