Screening, predicting, and computer experiments
Technometrics
Intrinsic Kriging and prior information: Research Articles
Applied Stochastic Models in Business and Industry - Statistical Learning
Nonlinear methods for inverse statistical problems
Computational Statistics & Data Analysis
Data-driven Kriging models based on FANOVA-decomposition
Statistics and Computing
Global sensitivity analysis of stochastic computer models with joint metamodels
Statistics and Computing
Computational Statistics & Data Analysis
Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package
Computational Statistics & Data Analysis
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Complex computer codes are often too time expensive to be directly used to perform uncertainty propagation studies, global sensitivity analysis or to solve optimization problems. A well known and widely used method to circumvent this inconvenience consists in replacing the complex computer code by a reduced model, called a metamodel, or a response surface that represents the computer code and requires acceptable calculation time. One particular class of metamodels is studied: the Gaussian process model that is characterized by its mean and covariance functions. A specific estimation procedure is developed to adjust a Gaussian process model in complex cases (non-linear relations, highly dispersed or discontinuous output, high-dimensional input, inadequate sampling designs, etc.). The efficiency of this algorithm is compared to the efficiency of other existing algorithms on an analytical test case. The proposed methodology is also illustrated for the case of a complex hydrogeological computer code, simulating radionuclide transport in groundwater.