Weighted stochastic response surface method considering sample weights |
| |
Authors: | Fenfen Xiong Wei Chen Ying Xiong Shuxing Yang |
| |
Affiliation: | (1) School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China;(2) Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA;(3) School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200031, China;(4) Bank of America, Charlotte, NC 28255, USA |
| |
Abstract: | Conventional stochastic response surface methods (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation
treat every sample point equally during the regression process and may produce inaccurate estimations of PCE coefficients.
To address this issue, a new weighted stochastic response surface method (WSRSM) that considers the sample probabilistic weights
in regression is studied in this work. Techniques for determining sample probabilistic weights for three sampling approaches
Gaussian Quadrature point (GQ), Monomial Cubature Rule (MCR), and Latin Hypercube Design (LHD) are developed. The advantage
of the proposed method is demonstrated through mathematical and engineering examples. It is shown that for various sampling
techniques WSRSM consistently achieves higher accuracy of uncertainty propagation without introducing extra computational
cost compared to the conventional SRSM. Insights into the relative accuracy and efficiency of various sampling techniques
in implementation are provided as well. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|