An intelligent polynomial chaos expansion method based upon features selection

Polynomial chaos expansion (PCE) method is a common tool for uncertainty quantification (UQ) in fluid mechanics. However, there exists 'Dimensional Curse' when the parameters dimension is very high, and large samples are required to solve the PCE function. This would hinder the application of PCE in high dimensions. An intelligent PCE method based on the idea of features selection in machine learning is proposed in this paper. Therefore, only several important features will be selected to construct the PCE function, then fewer samples will be needed to solve the model, and it will be more efficient. Several benchmark functions and an RAE2822 airfoil flow case are utilized to verify the UQ capability of the intelligent PCE. It is proved to be more efficient than the original PCE, with nearly same accuracy.