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
2022-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
