KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging
Author(s)
Date issued
2014
In
Computational Statistics & Data Analysis
No
71
From page
1021
To page
1034
Subjects
Computer experiments Gaussian process modeling Sequential design Probability of failure Contour line estimation Excursion set Active learning
Abstract
Several strategies relying on kriging have recently been proposed for adaptively estimating contour lines and excursion sets of functions under severely limited evaluation budget. The recently released R package KrigInv 3 is presented and offers a sound implementation of various sampling criteria for those kinds of inverse problems. KrigInv is based on the DiceKriging package, and thus benefits from a number of options concerning the underlying kriging models. Six implemented sampling criteria are detailed in a tutorial and illustrated with graphical examples. Different functionalities of KrigInv are gradually explained. Additionally, two recently proposed criteria for batch-sequential inversion are presented, enabling advanced users to distribute function evaluations in parallel on clusters or clouds of machines. Finally, auxiliary problems are discussed. These include the fine tuning of numerical integration and optimization procedures used within the computation and the optimization of the considered criteria.
Publication type
journal article