A review on design, modeling and applications of computer experiments

By V. Chen, Kwok Leung Tsui, Russell R. Barton, Martin Meckesheimer. 2006

337 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/16ce4ed742513faaa8e85b3cbaaa4112ae24b6a0

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In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Calculations of Sobol indices for the Gaussian process metamodel (2008, methodology): “Several metamodels are classically used: polynomials, splines, generalized linear models, or learning statistical models like neural networks, regression trees, support vector machines (Chen et al. [3], Fang et al. [8]).”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, background): “Hammersley sequences and uniform designs belong to a more general group called low discrepancy sequences [63] wherein Hammersley sampling is found to provide better uniformity than Latin Hypercube designs.”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “The reviews on computer design of experiments can be found in the references (Chen et al. 2003, 2006; Crary 2002; John and Draper 1975; Steinberg and Hunter 1984).”
An efficient lightweight design strategy for body-in-white based on implicit parameterization technique (2017, methodology): “Firstly, optimal Latin hypercube sampling (OLHS) technique (Park 1994; Chen et al. 2006) is adopted to generate 600 samples in the whole design space.”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “, 1978; Deming and Morgan, 1993; Myers and Montgomery, 1995; Hicks and Turner, 1999; Montgomery, 2001) including that on the Design of Computer Experiments (Sacks et al., 1989; Crary, 2002; Chen et al., 2006) where we typically have control over a variety of parameters and wish to understand their e”