Bayesian Optimisation with Gaussian Process Regression Applied to Fluid Problems

Rezaeiravesh S, Morita Y, Tabatabaei N, Vinuesa R, Fukagata K, Schlatter P (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 267

Pages Range: 137-143

Conference Proceedings Title: Springer Proceedings in Physics

Event location: Virtual, Online

ISBN: 9783030807153

DOI: 10.1007/978-3-030-80716-0_18

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.

Authors with CRIS profile

Philipp Schlatter

Involved external institutions

Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH) SE Sweden (SE) Keio University JP Japan (JP)

How to cite

APA:

Rezaeiravesh, S., Morita, Y., Tabatabaei, N., Vinuesa, R., Fukagata, K., & Schlatter, P. (2021). Bayesian Optimisation with Gaussian Process Regression Applied to Fluid Problems. In Ramis Örlü, Alessandro Talamelli, Joachim Peinke, Martin Oberlack (Eds.), Springer Proceedings in Physics (pp. 137-143). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Rezaeiravesh, Saleh, et al. "Bayesian Optimisation with Gaussian Process Regression Applied to Fluid Problems." Proceedings of the 9th iTi Conference on Turbulence, iTi 2021, Virtual, Online Ed. Ramis Örlü, Alessandro Talamelli, Joachim Peinke, Martin Oberlack, Springer Science and Business Media Deutschland GmbH, 2021. 137-143.

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