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Authors: A. Kaintura, D. Spina, I. Couckuyt, L.Knockaert, W. Bogaerts, T. Dhaene
Title: A Kriging and Stochastic Collocation ensemble for uncertainty quantification in engineering applications
Format: International Journal
Publication date: 3/2017
Journal/Conference/Book: Engineering with Computers
Editor/Publisher: Springer, 
Volume(Issue): p.1-15
Internal Reference: [N-2018]
DOI:
Download: Download this Publication (2.5MB) (2.5MB)

Abstract

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.

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