A Kriging and Stochastic Collocation ensemble for uncertainty quantification in engineering applications

作者：Kaintura A^*; Spina D; Couckuyt I; Knockaert L; Bogaerts W; Dhaene T

来源：Engineering with Computers, 2017, 33(4): 935-949.

DOI：10.1007/s00366-017-0507-0

• Surrogate modeling
• Stochastic Collocation
• Kriging
• Uncertainty quantification

We propose a new surrogate modeling approach by combining two non-intrusive techniques: Kriging and Stochastic Collocation. The proposed method relies on building a sufficiently accurate Stochastic Collocation model which acts as a basis to construct a Kriging model on the residuals, to combine the accuracy and efficiency of Stochastic Collocation methods in describing stochastic quantities with the flexibility and modeling power of Kriging-based approaches. We investigate and compare performance of the proposed approach with state-of-art techniques over benchmark problems and practical engineering examples on various experimental designs.

• 出版日期2017-10