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Research Article | Open Access

A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics

Giulio Ortali1,2, Nicola Demo1( ), Gianluigi Rozza1
Mathematics Area, mathLab, SISSA, via Bonomea 265, I-34136 Trieste, Italy
Department of Applied Physics, Eindhoven University of Technology, The Netherlands
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Abstract

This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the Stokes problem, and in the following to a real-world industrial problem, within a shape optimization pipeline for a naval engineering problem.

Keywords

data-driven methodreduced order modelingGaussian Process Regressionparametric design problem

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Mathematics in Engineering
Volume 4 Issue 3,
June 2022
Pages 1-16
DOI: 10.3934/mine.2022021

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Cite this article:
Ortali G, Demo N, Rozza G. A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics. Mathematics in Engineering, 2022, 4(3): 1-16. https://doi.org/10.3934/mine.2022021

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Received: 22 November 2020
Accepted: 20 July 2021
Published: 15 June 2021
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)