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This work describes the implementation of a data-driven approach for thereduction of the complexity of parametrical partial differential equations(PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian ProcessRegression (GPR). This approach is applied initially to a literature case, thesimulation of the stokes problems, and in the following to a real-worldindustrial problem, inside a shape optimization pipeline for a navalengineering problem.

Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems