This class generates a new
model.class for Model4 and Model2. Based on the previous
estimation of the Gaussian process in the function
model, the design of experiments previously used
is improved according to [Damblin et al. 2018]. The aim is to reduce the
error produced by the initial estimation of the Gaussian process by fortifying the initial DOE. The method consists
in proposing new points based on the expectancy improvement criterion.
Fields should not be changed or manipulated by the user as they are updated internally during the estimation process.
An object of class
R6ClassGenerator of length 24.
the initial DOE used to fit the first Gaussian process
the initial Gaussian process generated in
the new Gaussian process fortified with the new design points
the number of parameter
the initial model
the new model
the initial calibrated model
the new calibrated model
the data set
minimum of the sum of squares used in the algorithm
DAMBLIN, Guillaume, BARBILLON, Pierre, KELLER, Merlin, et al. Adaptive numerical designs for the calibration of computer codes. SIAM/ASA Journal on Uncertainty Quantification, 2018, vol. 6, no 1, p. 151-179.
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