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• W2983821475 endingPage "106728" @default.
• W2983821475 startingPage "106728" @default.
• W2983821475 abstract "Simulation plays a major role in the conception, the optimization and the certification of complex systems. Of particular interest here is the calibration of the parameters of computer models from high-dimensional physical observations. When the run times of these computer codes is high, this work focuses on the numerical challenges associated with the statistical inference. In particular, several adaptations of the Gaussian Process Regression (GPR) to the high-dimensional or functional output case are presented for the emulation of computer codes from limited data. Then, an adaptive procedure is detailed to minimize the calibration parameters uncertainty at the minimal computational cost. The proposed method is eventually applied to two applications that are based on dynamic simulators." @default.
• W2983821475 created "2019-11-22" @default.
• W2983821475 creator A5032363972 @default.
• W2983821475 date "2020-04-01" @default.
• W2983821475 modified "2023-09-25" @default.
• W2983821475 title "Adaptive calibration of a computer code with time-series output" @default.
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• W2983821475 doi "https://doi.org/10.1016/j.ress.2019.106728" @default.
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