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 problems, and in the following to a real-world industrial problem, inside a shape optimization pipeline for a naval engineering problem.
@article{arxiv.2012.01989,
title = {Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems},
author = {Giulio Ortali and Nicola Demo and Gianluigi Rozza},
journal= {arXiv preprint arXiv:2012.01989},
year = {2024}
}