TY - GEN
T1 - A new weighted stochastic response surface method for uncertainty propagation
AU - Xiong, Fenfen
AU - Chen, Wei
AU - Xiong, Ying
AU - Yang, Shuxing
N1 - Funding Information:
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.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.2514/6.2010-9082
DO - 10.2514/6.2010-9082
M3 - Conference contribution
AN - SCOPUS:84880780724
SN - 9781600869549
T3 - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
BT - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
T2 - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010
Y2 - 13 September 2010 through 15 September 2010
ER -