D
David Ginsbourger
Researcher at University of Bern
Publications - 108
Citations - 4382
David Ginsbourger is an academic researcher from University of Bern. The author has contributed to research in topics: Gaussian process & Kriging. The author has an hindex of 31, co-authored 103 publications receiving 3688 citations. Previous affiliations of David Ginsbourger include Idiap Research Institute & École Normale Supérieure.
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Journal ArticleDOI
DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization
TL;DR: The versatility of DiceKriging with respect to trend and noise specifications, covariance parameter estimation, as well as conditional and unconditional simulations are illustrated on the basis of several reproducible numerical experiments.
Book ChapterDOI
Kriging is well-suited to parallelize optimization
TL;DR: This work investigates a multi-points optimization criterion, the multipoints expected improvement (\(q-{\mathbb E}I\)), aimed at choosing several points at the same time, and proposes two classes of heuristic strategies meant to approximately optimize the Q-EI, and applies them to the classical Branin-Hoo test-case function.
Journal ArticleDOI
Sequential design of computer experiments for the estimation of a probability of failure
TL;DR: SUR (stepwise uncertainty reduction) strategies are derived from a Bayesian formulation of the problem of estimating a probability of failure of a function f using a Gaussian process model of f and aim at performing evaluations of f as efficiently as possible to infer the value of the probabilities of failure.
Journal ArticleDOI
Adaptive Designs of Experiments for Accurate Approximation of a Target Region
TL;DR: An iterative strategy to build designs of experiments is proposed, which is based on an explicit trade-off between reduction of global uncertainty and exploration of the regions of interest, which shows that a substantial reduction of error can be achieved in the crucial regions.
Journal ArticleDOI
A benchmark of kriging-based infill criteria for noisy optimization
TL;DR: A comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided, and the three most intuitive criteria are found as poor alternatives.