Sampling Strategies for Computer Experiments: Design and Analysis

By Timothy W. Simpson, L. Dennis, Wei Chen. 2001

461 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d904bd908cbdf0a2e83b6a2bd06b12fa243a2756

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Citations in the corpus

Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence (2013, background): “Extensive overviews can be found in [50] [51] [52] [53] [54], two popular examples are the 'maximin' and 'uniform' Latin hypercube designs [50] [51].”
On Sequential Sampling for Global Metamodeling in Engineering Design (2002, background): “[9] compared five sampling strategies and four metamodeling approaches in terms of their”
Pitfalls of using a single criterion for selecting experimental designs (2008, background): “There have been some studies on the influence of different experimental designs (EDs) on predictions [1–4].”
Approximation methods in multidisciplinary analysis and optimization: a panel discussion (2004, methodology): “The panel consisted of five representatives from industry and government: (1) Andrew J. Booker from The Boeing Company, (2) Dipankar Ghosh from Vanderplaats Research & Development, ( 3 ) Anthony A. Giunta from Sandia National Laboratories, (4) Patrick N. Koch from Engineous Software, and (5) Ren-Jye”
A new adaptive response surface method for reliability analysis (2013, methodology): “Choice of experimental design In determistic computer experiments, space filling design are often prefered compared to classical design of experiments used in physical experiments [40,45,44].”
Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging (2008, methodology): “The sampling quality being essential to obtain an accurate model [14], section 4 compares different distribution methods.”
Kriging metamodeling in simulation: A review (2009, background): “, maximin designs and orthogonal arrays) are discussed in [26], including many websites and references; also see [5,17,19,31,48]; a Bayesian view is presented in [6].”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, unknown): “[29].”
Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not (2008, background): “50,51,52 Other researchers also started investigating the use of gradient information to facilitate metamodel construction.”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “Simpson et al. (2001a) compared and contrasted five types of experimental design and four types of approximation model.”
Investigation of an adaptive sampling method for data interpolation using radial basis functions (2010, methodology): “Radial basis function (RBF) interpolation is used to model the data, a method recommended by comparative studies [3, 6, 7] as being well suited to modelling highly non-linear functions using a small number of samples.”