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Computer Experiments (computer + experiment)

Distribution by Scientific Domains
Mathematics and Statistics63%

Selected Abstracts

Design and Modeling for Computer Experiments by K.-T.

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 4 2006
A. Sudjianto, R. Li
No abstract is available for this article. [source]

Meshing noise effect in design of experiments using computer experiments

ENVIRONMETRICS, Issue 5-6 2002
J. P. Caire
Abstract This work is intended to show the influence of grid length and meshing technique on the empirical modeling of current distribution in an industrial electroplating reactor. This study confirms the interest of usual DOEs for computer experiments. Any 2D mesh generator induced, in this sensitive case, a significant noise representing only less than 5 per cent of the response. The ,experimental error' obeys a normal distribution and the associated replicate SDs represents 20 per cent of the global residual standard deviation. The geometry seems also to influence the corresponding noise. If the current density uniformity could be considered as a severe test, it is obvious that the noise generated by meshing would be amplified for 3D grids that will be in common use in future years. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Adaptive multiobjective optimization of process conditions for injection molding using a Gaussian process approach

ADVANCES IN POLYMER TECHNOLOGY, Issue 2 2007
Jian Zhou
Abstract Selecting the proper process conditions for the injection-molding process is treated as a multiobjective optimization problem, where different objectives, such as minimizing the injection pressure, volumetric shrinkage/warpage, or cycle time, present trade-off behaviors. As such, various optima may exist in the objective space. This paper presents the development of an integrated simulation-based optimization system that incorporates the design of computer experiments, Gaussian process (GP) for regression, multiobjective genetic algorithm (MOGA), and levels of adjacency to adaptively and automatically search for the Pareto-optimal solutions for different objectives. Since the GP approach can provide both the predictions and the estimations of the predictions simultaneously, a nondominated sorting procedure on the predicted variances at each iteration step is performed to intelligently select extra samples that can be used as additional training samples to improve the GP surrogate models. At the same time, user-defined adjacency constraint percentages are employed for evaluating the convergence of iteration. The illustrative applications in this paper show that the proposed optimization system can help mold designers to efficiently and effectively identify optimal process conditions. © 2007 Wiley Periodicals, Inc. Adv Polym Techn 26:71,85, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/adv.20092 [source]

Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach

POLYMER ENGINEERING & SCIENCE, Issue 5 2007
Jian Zhou
This article presents an integrated, simulation-based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn-around time and adequate accuracy for substituting time-consuming computer simulations during system-level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684,694, 2007. © 2007 Society of Plastics Engineers. [source]

Analysis of computer experiments with multiple noise sources

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 2 2010
Christian Dehlendorff
Abstract In this paper we present a modeling framework for analyzing computer models with two types of variations. The paper is based on a case study of an orthopedic surgical unit, which has both controllable and uncontrollable factors. Our results show that this structure of variation can be modeled effectively with linear mixed effects models and generalized additive models. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Design and analysis for the Gaussian process model,

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2009
Bradley Jones
Abstract In an effort to speed the development of new products and processes, many companies are turning to computer simulations to avoid the time and expense of building prototypes. These computer simulations are often complex, taking hours to complete one run. If there are many variables affecting the results of the simulation, then it makes sense to design an experiment to gain the most information possible from a limited number of computer simulation runs. The researcher can use the results of these runs to build a surrogate model of the computer simulation model. The absence of noise is the key difference between computer simulation experiments and experiments in the real world. Since there is no variability in the results of computer experiments, optimal designs, which are based on reducing the variance of some statistic, have questionable utility. Replication, usually a ,good thing', is clearly undesirable in computer experiments. Thus, a new approach to experimentation is necessary. Published in 2009 by John Wiley & Sons, Ltd. [source]

Assessment of uncertainty in computer experiments from Universal to Bayesian Kriging

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2009
C. Helbert
Abstract Kriging was first introduced in the field of geostatistics. Nowadays, it is widely used to model computer experiments. Since the results of deterministic computer experiments have no experimental variability, Kriging is appropriate in that it interpolates observations at data points. Moreover, Kriging quantifies prediction uncertainty, which plays a major role in many applications. Among practitioners we can distinguish those who use Universal Kriging where the parameters of the model are estimated and those who use Bayesian Kriging where model parameters are random variables. The aim of this article is to show that the prediction uncertainty has a correct interpretation only in the case of Bayesian Kriging. Different cases of prior distributions have been studied and it is shown that in one specific case, Bayesian Kriging supplies an interpretation as a conditional variance for the prediction variance provided by Universal Kriging. Finally, a simple petroleum engineering case study presents the importance of prior information in the Bayesian approach. Copyright © 2009 John Wiley & Sons, Ltd. [source]

A note on the choice and the estimation of Kriging models for the analysis of deterministic computer experiments

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2009
David Ginsbourger
Abstract Our goal in the present article to give an insight on some important questions to be asked when choosing a Kriging model for the analysis of numerical experiments. We are especially concerned about the cases where the size of the design of experiments is relatively small to the algebraic dimension of the inputs. We first fix the notations and recall some basic properties of Kriging. Then we expose two experimental studies on subjects that are often skipped in the field of computer simulation analysis: the lack of reliability of likelihood maximization with few data and the consequences of a trend misspecification. We finally propose an example from a porous media application, with the introduction of an original Kriging method in which a non-linear additive model is used as an external trend. Copyright © 2009 John Wiley & Sons, Ltd. [source]