Abstract
Conventional stochastic response surface method (SRSM) based on polynomial chaos expansion (PCE) for uncertainty propagation treats every sample points equally during the regression process and may produce inaccurate coefficient estimations in PCE. A new weighted stochastic response surface method (WSRSM) to overcome such limitation by considering the sample probabilistic weights in regression is studied in this work. Techniques that associate sample probabilistic weights to different sampling approaches such as Gaussian Quadrature point (GQ), Monomial Cubature Rule (MCR) and Latin Hypercube Design (LHD) are developed. The proposed method is demonstrated by several mathematical and engineering examples. Results show that for various sampling techniques, WSRSM can consistently improve the accuracy of uncertainty propagation compared to the conventional SRSM without adding extra computational cost. Insights into the relative accuracy and efficiency of using various sampling techniques in implementation are provided.
| Original language | English (US) |
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| Title of host publication | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010 |
| DOIs | |
| State | Published - 2010 |
| Event | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010 - Ft. Worth, TX, United States Duration: Sep 13 2010 → Sep 15 2010 |
Publication series
| Name | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010 |
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Other
| Other | 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010 |
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| Country/Territory | United States |
| City | Ft. Worth, TX |
| Period | 9/13/10 → 9/15/10 |
Funding
The grant support from National Science Foundation of China (10972034) and National Science Foundation (CMMI – 0928320) are greatly acknowledged. The views expressed are those of the authors and do not necessarily reflect the views of the sponsors.
ASJC Scopus subject areas
- Aerospace Engineering
- Mechanical Engineering