【摘 要】
：
Uncertainty propagation (UP) methods,which quantify uncertainty in system output performance based on random or noisy inputs,are of great importance for design under uncertainty especially for those i
【机 构】
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School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, P.R.China
【出 处】
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Asian Congress of Structural and Multidisciplinary Optimizat
论文部分内容阅读
Uncertainty propagation (UP) methods,which quantify uncertainty in system output performance based on random or noisy inputs,are of great importance for design under uncertainty especially for those involving computational intensive simulation models.To improve the accuracy of UP,the polynomial-chaos-kriging (PCK) method has been derived as a new UP approach through combining PCE and Kriging.In the current PCK method,all the sample points are generated all at once to calculate the parameters of PCK model for UP.However,for some computational expensive problems of black-box-type,it is difficult to determine a reasonable number of sample points for UP beforehand to ensure good accuracy and efficiency.
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