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

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

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, t...

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Bibliographic Details
Main Authors: Giulio Ortali, Nicola Demo, Gianluigi Rozza
Format: Article
Language:English
Published: AIMS Press 2022-05-01
Series:Mathematics in Engineering
Subjects:
data-driven method
reduced order modeling
gaussian process regression
parametric design problem
Online Access:https://www.aimspress.com/article/doi/10.3934/mine.2022021?viewType=HTML

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