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International Journal for Uncertainty Quantification
IF: 4.911 5-Year IF: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2019028125
pages 569-587


Die Joseph Hassan Millogo
Department of Mechanical Engineering, National Taiwan University, Taiwan
Kuei-Yuan Chan
Department of Mechanical Engineering, National Taiwan University, Taiwan


Data analysis deciphers phenomena and system behaviors within a large number of experimental realizations. Transforming these massive quantities of raw data into knowledge about the data is made possible thanks to continuously improved computing techniques. In science and engineering, a particular interest lies within surrogate models for system behaviour prediction and data extrapolation. These models could, however, be under- or over- fitted when confronted to a complex dataset or one embedded with uncertainty. In this paper, we suggest a treatment approach of experimental data under uncertainty prior to its surrogate model creation. We specially focus on extrapolation an attempt to estimate the true underlying phenomena. We quantify the uncertainty quantity through eigenvalues, copy the behavior of the data through its covariance matrix, and reproduce an almost identical dataset whose particularity is a perfectly correlated inputs and output. This new dataset is then used as the basis for the creation of a surrogate model. Our approach shows consistency and a clear opportunity to obtain better predictions under uncertainty as it focuses on the overall dataset's behavior and stays faithful to each data.


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