  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.
