Our goal in the present work is to give an insight on some important questions to be asked when choosing a Kriging model for the analysis of numerical experiments. We are especially concerned about the cases where the size of the design of experiments is small relatively to the algebraic dimension of the inputs. We first fix the notations and recall some basic properties of Kriging. Then we expose two experimental studies on subjects that are often skipped in the field of computer simulation analysis: the lack of reliability of likelihood maximization with few data, and the consequences of a trend misspecification. We finally propose an example from a porous media application, with the introduction of an original Kriging method in which a n...
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approxi...
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approxi...
Nowadays it is common to reproduce physical systems using mathematical simulation models and, despit...
International audienceOur goal in the present article to give an insight on some important questions...
Processes are so complex in many areas of science and technology that physical experimentation is of...
<div><p>Kriging is commonly used for developing emulators as surrogates for computationally intensiv...
We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and th...
Scientists and engineers commonly use simulation models to study real systems for which actual exper...
The use of Kriging surrogate models has become popular in approximating computation-intensive determ...
Many scientific disciplines use mathematical models to describe complicated real systems. Often, ana...
This article reviews Kriging (also called spatial correlation modeling). It presents the basic Krigi...
DoctoralThis is a two hours class on conditional Gaussian processes, i.e., kriging. We attempt to s...
Computer simulations are often used to replace physical experiments aimed at exploring the complex r...
Kriging, or Gaussian process modeling, is widely used in estimating unknown functions based on the (...
The use of surrogate models for approximating computationally expensive simulations has been on the ...
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approxi...
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approxi...
Nowadays it is common to reproduce physical systems using mathematical simulation models and, despit...
International audienceOur goal in the present article to give an insight on some important questions...
Processes are so complex in many areas of science and technology that physical experimentation is of...
<div><p>Kriging is commonly used for developing emulators as surrogates for computationally intensiv...
We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and th...
Scientists and engineers commonly use simulation models to study real systems for which actual exper...
The use of Kriging surrogate models has become popular in approximating computation-intensive determ...
Many scientific disciplines use mathematical models to describe complicated real systems. Often, ana...
This article reviews Kriging (also called spatial correlation modeling). It presents the basic Krigi...
DoctoralThis is a two hours class on conditional Gaussian processes, i.e., kriging. We attempt to s...
Computer simulations are often used to replace physical experiments aimed at exploring the complex r...
Kriging, or Gaussian process modeling, is widely used in estimating unknown functions based on the (...
The use of surrogate models for approximating computationally expensive simulations has been on the ...
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approxi...
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approxi...
Nowadays it is common to reproduce physical systems using mathematical simulation models and, despit...