Description Usage Format Fields References

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.

1 |

An object of class `R6ClassGenerator`

of length 24.

`doe.init`

the initial DOE used to fit the first Gaussian process

`GP.init`

the initial Gaussian process generated in

`model`

function`GP.new`

the new Gaussian process fortified with the new design points

`p`

the number of parameter

`md`

the initial model

`md.new`

the new model

`mdfit`

the initial calibrated model

`mdfit.new`

the new calibrated model

`X`

the data set

`m`

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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