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

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.

Keywords

• data-driven method
• reduced order modeling
• gaussian process regression
• parametric design problem