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As stated before, solving the kriging systems is equivalent to solving the corresponding MME.
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In the section Kriging Interpolation solution, we have seen how to solve the Kriging interpolation.
Under both models, parameters and hidden variables are estimated via maximum likelihood (ML) and BLUP of the unknowns is established by solving the corresponding linear kriging systems.
To build the IK model, we employ the genetic algorithm (GA) method to find the Kriging hyperparameters θ, by solving the maximum likelihood equation (MLE).
EMLK is a sophisticated kriging method where more efficient results are obtained by solving the prediction problem in the Gaussian domain via a normal scores transform of the sample data (a logarithmic or similar transform does not completely ensure normality, whereas a normal scores transform does).
A robust optimization algorithm integrating Kriging models and nested GA is proposed to directly solve the constrained interval robust optimization model of the uncertain structure.
Ordinary Kriging and Design of Experiments (DOE) approaches are used to construct the surrogate models by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings.
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