Probabilistic Sensitivity Analysis in Engineering Design Using Uniform Sampling and Saddlepoint Approximation

2005-01-0344

04/11/2005

Event
SAE 2005 World Congress & Exhibition
Authors Abstract
Content
Sensitivity analysis plays an important role to help engineers gain knowledge of complex model behaviors and make informed decisions regarding where to spend engineering effort. In design under uncertainty, probabilistic sensitivity analysis (PSA) is performed to quantify the impact of uncertainties in random variables on the uncertainty in model outputs. One of the most challenging issues for PSA is the intensive computational demand for assessing the impact of probabilistic variations. An efficient approach to PSA is presented in this article. Our approach employs the Kolmogorov-Smirnov (KS) distance to quantify the importance of input variables. The saddlepoint approximation approach is introduced to improve the efficiency of generating cumulative distribution functions (CDFs) required for the evaluation of the KS distance. To further improve efficiency, optimized uniform samples are used to replace the direct Monte Carlo simulations for determining the cumulant generating function (CGF) in saddlepoint approximation. Efficient construction of a uniform design necessary to generate the “best” samples in a multidimensional space is presented. Our approach is illustrated with a structural design problem. It has the potential to be the most beneficial for high dimensional engineering design problems that involve expensive computer simulations.
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DOI
https://doi.org/10.4271/2005-01-0344
Pages
12
Citation
Sudjianto, A., Du, X., and Chen, W., "Probabilistic Sensitivity Analysis in Engineering Design Using Uniform Sampling and Saddlepoint Approximation," SAE Technical Paper 2005-01-0344, 2005, https://doi.org/10.4271/2005-01-0344.
Additional Details
Publisher
Published
Apr 11, 2005
Product Code
2005-01-0344
Content Type
Technical Paper
Language
English