The Design and Analysis of Computer Experiments
By Thomas J. Santner, Brett Williams, William I. Notz. Springer Series in Statistics, 2003
1,553 citations recorded by the source corpus.
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/cb9ba621dcdbbdebc8a5957e7e84f6122a59b6da
How researchers see this paper
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In Management Science and Operations Research
- Yaochu Jin
- Victor Picheny
- Timothy W. Simpson
- Peter I. Frazier
- David Ginsbourger
- Felipe Antonio Chegury Viana
- Nando de Freitas
- G. G. Wang
Citations in the corpus
- Sequential parameter optimization (2005, methodology): “Following [ 2 ], the response is modeled as a realization of a regression model and a random process.”
- Recent advances in surrogate-based optimization (2009, methodology): “We will examine how the information is incorporated into a Gaussian process based RBF using a form of co-Kriging [64].”
- An EMO Algorithm Using the Hypervolume Measure as Selection Criterion (2005, methodology): “5.1 Integration of Fitness Function Approximations We use Kriging metamodels [ 15 ] as fitness function approximation tools to accelerate the SMS-EMOA.”
- Approximation methods in multidisciplinary analysis and optimization: a panel discussion (2004, methodology): “ Sacks et al. (1989) state that the “classical” notions of blocking, replication, and randomization are irrelevant when it comes to deterministic computer experiments.”
- Update strategies for kriging models used in variable fidelity optimization (2006, methodology): “However, global metamodels have been successfully used as approximations of the more expensive computer simulations in the design process (Booker 1998;Simpsonetal.2001a).Aprominentmethodtoglobally approximate deterministic computer simulations is kriging; however,krigingmodelscanbeacomputationalburd”
- Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models (2009, methodology): “[47] T.”
- Controlled stratification for quantile estimation (2008, methodology): “Recently, some authors have taken advantage of one particular type of metamodel: the Gaussian process model ( Sacks et al., 1989 ) which gives not only a predictor (the mean) of a computer experiment but also a local indicator of prediction accuracy (the variance).”