| 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 | | DOI: | 10.1007/s00366-017-0507-0 | | Citations: | 13 (Dimensions.ai - last update: 24/11/2024)
11 (OpenCitations - last update: 27/6/2024)
Look up on Google Scholar
| | Download: |
 (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. Related Research Topics
Related Projects
|
|
|
|
|
Citations (OpenCitations)
|
|
