Bayesian Optimisation with Gaussian Process Regression Applied to Fluid Problems

Saleh Rezaeiravesh, Yuki Morita, Narges Tabatabaei, Ricardo Vinuesa, Koji Fukagata, Philipp Schlatter

研究成果: Conference contribution

抄録

Bayesian optimisation based on Gaussian process regression (GPR) is an efficient gradient-free algorithm widely used in various fields of data sciences to find global optima. Based on a recent study by the authors, Bayesian optimisation is shown to be applicable to optimisation problems based on simulations of different fluid flows. Examples range from academic to more industrially-relevant cases. As a main conclusion, the number of flow simulations required in Bayesian optimisation was found not to exponentially grow with the dimensionality of the design parameters (hence, no curse of dimensionality). Here, the Bayesian optimisation method is outlined and its application to the shape optimisation of a two-dimensional lid-driven cavity flow is detailed.

本文言語English
ホスト出版物のタイトルProgress in Turbulence IX - Proceedings of the iTi Conference in Turbulence, 2021
編集者Ramis Örlü, Alessandro Talamelli, Joachim Peinke, Martin Oberlack
出版社Springer Science and Business Media Deutschland GmbH
ページ137-143
ページ数7
ISBN(印刷版)9783030807153
DOI
出版ステータスPublished - 2021
イベント9th iTi Conference on Turbulence, iTi 2021 - Virtual, Online
継続期間: 2021 2月 252021 2月 26

出版物シリーズ

名前Springer Proceedings in Physics
267
ISSN(印刷版)0930-8989
ISSN(電子版)1867-4941

Conference

Conference9th iTi Conference on Turbulence, iTi 2021
CityVirtual, Online
Period21/2/2521/2/26

ASJC Scopus subject areas

  • 物理学および天文学(全般)

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