Bayesian Optimisation with Gaussian Process Regression Applied to Fluid Problems

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProgress in Turbulence IX - Proceedings of the iTi Conference in Turbulence, 2021
EditorsRamis Örlü, Alessandro Talamelli, Joachim Peinke, Martin Oberlack
PublisherSpringer Nature
Pages137-143
Number of pages7
ISBN (Print)9783030807153
DOIs
Publication statusPublished - 2021
Event9th iTi Conference on Turbulence, iTi 2021 - Virtual, Online
Duration: 25 Feb 202126 Feb 2021

Publication series

NameSpringer Proceedings in Physics
Volume267
ISSN (Print)0930-8989
ISSN (Electronic)1867-4941

Conference

Conference9th iTi Conference on Turbulence, iTi 2021
CityVirtual, Online
Period25/02/2126/02/21

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