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- W2596090644 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." @default.
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- W2596090644 date "2017-03-17" @default.
- W2596090644 modified "2023-10-16" @default.
- W2596090644 title "A Kriging and Stochastic Collocation ensemble for uncertainty quantification in engineering applications" @default.
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- W2596090644 doi "https://doi.org/10.1007/s00366-017-0507-0" @default.
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