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.

A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics / Ortali, Giulio; Demo, Nicola; Rozza, Gianluigi. - In: MATHEMATICS IN ENGINEERING. - ISSN 2640-3501. - 4:3(2022), pp. 1-16. [10.3934/mine.2022021]

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

Giulio Ortali;Nicola Demo
;
Gianluigi Rozza
2022

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.
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10.3934/mine.2022021
Ortali, Giulio; Demo, Nicola; Rozza, Gianluigi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/130170
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