Designs for Computer Experiments

Sacks, Jerome; Schiller, Susannah B.; Welch, William J.

doi:10.1080/00401706.1989.10488474

Cited by 685 publications

(215 citation statements)

References 19 publications

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“…Indeed, they have been -investigated since the beginning of numerical solutions; Richardson in 1910 [7]. These deficiencies in the solution of the discrete equations are properly called errors because they are approximations to ' the solutions of the original PDE's.…”

Section: Conceptual Modeling Uncertaintiesmentioning

confidence: 99%

“…Therefore x=yo+Me+& (7) where the test variance is modeled as &-N(O, o:), a normal distribution with zero mean . and variance c:.…”

Section: Revising Prior Probabilities To Account For New Datamentioning

confidence: 99%

“…In order to proceed, it is now necessary to develop a mathematical framework for the consideration of multiple models. This mathematical framework is based on probability and statistics, specifically Bayesian statistical inference [1,5,6,7,8,9,10,11]. The development here follows from work first presented by Draper [ 1].…”

Section: Assessment and Propagation Of Model Uncertainty Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Methodology for characterizing modeling and discretization uncertainties in computational simulation

Alvin¹,

Oberkampf²,

Rutherford³

et al. 2000

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Section: Conceptual Modeling Uncertaintiesmentioning

confidence: 99%

“…Therefore x=yo+Me+& (7) where the test variance is modeled as &-N(O, o:), a normal distribution with zero mean . and variance c:.…”

Section: Revising Prior Probabilities To Account For New Datamentioning

confidence: 99%

Section: Assessment and Propagation Of Model Uncertainty Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Methodology for characterizing modeling and discretization uncertainties in computational simulation

Alvin¹,

Oberkampf²,

Rutherford³

et al. 2000

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“…al. [10] introduced a method of minimization of the integrated mean square error (IMSE). Shewry and Wynn performed selection of designs based on the maximization of entropy.…”

Section: Related Workmentioning

confidence: 99%

An Extended Latin Hypercube Sampling Approach for Cad Model Generation

Khan¹,

Günpinar²

2017

ANADOLU UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY a - Applied Sciences and Engineering

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In this paper, extended version of Latin hypercube sampling (ELHS) is proposed to obtain different design variations of a CAD model. The model is first represented by design parameters. Design constraints that are relationships between the parameters are then determined. After assigning value ranges for the design parameters, design space is formed. Each design parameter represents a dimension of this design space. Design is a point in the design space and is feasible if it satisfies the predefined design constraints. Otherwise, it is infeasible. ELHS utilizes an input design in order to obtain feasible designs.All dimensions of the design space are divided into equal number of intervals. ELHS perform trials in design space to find feasible designs. In each trial, all the candidate designs are enumerated and one of them is selected based on a cost function. Value of the cost function is zero if all design constraints of the design are satisfied. A similarity constraint is introduced in order to eliminate designs with similar geometries. Three different CAD models are utilized for this study's experiments in order to show the results of the ELHS algorithm.

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“…Jin et al (2000), Simpson et al (2001) and Wang and Shan (2006) provided an overview of the most commonly used surrogate modelling techniques. Kriging, which was proposed by Sacks et al (1989b) for the design and analysis of computer experiments, is arguably popular to approximate deterministic data resulting from computer codes (Sacks et al 1989a;Kleijnen 2009). One of the primary goals of surrogate modelling is to enhance the accuracy of surrogate mod-els with additional data which are not computationintensive.…”

Section: Introductionmentioning

confidence: 99%

Performance study of multi-fidelity gradient enhanced kriging

Ulaganathan

Couckuyt

Ferranti

et al. 2014

Struct Multidisc Optim

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Multi-fidelity surrogate modelling offers an efficient way to approximate computationally expensive simulations. In particular, Kriging-based surrogate models are popular for approximating deterministic data. In this work, the performance of Kriging is investigated when multi-fidelity gradient data is introduced along with multi-fidelity function data to approximate computationally expensive black-box simulations. To achieve this, the recursive CoKriging formulation is extended by incorporating multi-fidelity gradient information. This approach, denoted by Gradient-Enhanced recursive CoKriging (GECoK), is initially applied to two analytical problems. As expected, results from the analytical benchmark problems show that additional gradient information of different fidelities can significantly improve the accuracy of the Kriging model. Moreover, GECoK provides a better approximation even when the gradient information is only partially available. Further comparison between CoKriging, Gradient Enhanced

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Designs for Computer Experiments

Cited by 685 publications

References 19 publications

Methodology for characterizing modeling and discretization uncertainties in computational simulation

Methodology for characterizing modeling and discretization uncertainties in computational simulation

An Extended Latin Hypercube Sampling Approach for Cad Model Generation

Performance study of multi-fidelity gradient enhanced kriging

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