A fast evaluation method of surrogate model based on active subspace dimension reduction

Abstract

Abstract Aimed at the problem of high-dimensional uncertainty quantification commonly existing in engineering complex shapes, traditional methods are often time-consuming and energy-consuming. So the paper proposes an efficient surrogate model method based on active subspace dimension reduction. The method uses active subspace to achieve feature dimension reduction and construct surrogate model, and develops a fast evaluation method of 1-D active subspace based on polynomial least squares decomposition to solve the difficult derivative problems. The detailed implementation process of the method is given in the paper, and the feasibility of the method is verified by examples such as the ablation thermal response and RAE2822 airfoil. It provides a solution for the quantification of multivariate uncertainty in engineering.