@article{Ortali2022, 
author = {Giulio Ortali and Nicola Demo and Gianluigi Rozza},
title = {A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics},
year = {2022},
journal = {Mathematics in Engineering},
volume = {4},
number = {3},
pages = {1-16},
keywords = {data-driven method, reduced order modeling, Gaussian Process Regression, parametric design problem},
url = {https://www.sciopen.com/article/10.3934/mine.2022021},
doi = {10.3934/mine.2022021},
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.}
}