Design and Modeling for Computer Experiments

By K. Fang, Runze Li, Agus Sudjianto. 2005

1,110 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/542081940d24f9fdee937612aed7d8b3f7d69b0c

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In Computational Theory and Mathematics

Citations in the corpus

Optimal maximin $L_{1}$-distance Latin hypercube designs based on good lattice point designs (2018, background): “Representative designs include Latin hypercube designs (LHDs) and their modifications, maximin distance designs [Johnson, Moore and Ylvisaker (1990)] and uniform designs [Fang and Wang (1994)]. LHDs have uniform one-dimensional projections and orthogonal-array based LHDs [Tang (1993), He and Tang (2”
Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W (2014, methodology): “To understand how the melt-pool characteristics, including the length, width, and depth, would change with power and scan speed, we conducted a full fractional design of computer experiments (Oehlert (2000) and Fang et al. (2005)) using the Eagar-Tsai model.”
Stochastic kriging for simulation metamodeling (2008, methodology): “(2003) and Fang et al. (2006). Here we focus on extending the most well-known method— maximum likelihood—to allow for intrinsic uncertainty.”
Uniform fractional factorial designs (2012, methodology): “Various discrepancies have been used as measures of uniformity; see Fang et al. (2000) and Fang, Li and Sudjianto (2006). These discrepancies all have their geometrical meanings and can be interpreted as the difference between the empirical distribution and the uniform distribution. Among them, the ”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, positive): “In the early 2000s, applied mathematicians formally working on ‘design and analysis of computer experiments ’ (DACE) started building linkages with SA (Santner et al., 2003; Fang et al., 2005).”
Minimax and maximin space-filling designs: some properties and methods for construction (2017, neutral): “See also Fang et al. (2006, Chap.”
Gaussian Process Models for Computer Experiments With Qualitative and Quantitative Factors (2008, background): “(For detailed discussions of such models, see Santner, Williams, and Notz 2003; Fang, Li, and Sudjianto 2005.)”
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]).”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, background): “Also, our focus is primarily on the surrogate modeling techniques themselves, not the experimental designs used to generate sample data; interested readers are referred to recent overviews and texts on the topic [11,12].”
A Review on Global Sensitivity Analysis Methods (2014, positive): “The most used metamodels include polynomials, splines, generalized linear models, generalized additive models, kriging, neural networks, SVM, boosting regression trees (Simpson et al. [82], Fang et al. [24]).”