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Journal ArticleDOI

Review of Metamodeling Techniques in Support of Engineering Design Optimization

Gongming Wang, +1 more
- Vol. 129, Iss: 4, pp 370-380
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TLDR
This work reviews the state-of-the-art metamodel-based techniques from a practitioner's perspective according to the role of meetamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems.
Abstract
Computation-intensive design problems are becoming increasingly common in manufacturing industries. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. To address such a challenge, approximation or metamodeling techniques are often used. Metamodeling techniques have been developed from many different disciplines including statistics, mathematics, computer science, and various engineering disciplines. These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design optimization. This work reviews the state-of-the-art metamodel-based techniques from a practitioner’s perspective according to the role of metamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems. Challenges and future development of metamodeling in support of engineering design is also analyzed and discussed.Copyright © 2006 by ASME

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Citations
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Minimization of Accident Severity Index in concrete barrier designs using an ensemble of radial basis function metamodel-based optimization

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Metamodel-based inverse method for parameter identification: elastic–plastic damage model

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Forming limit of local loading forming of Ti-alloy large-scale rib-web components considering defects in the transitional region

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Statistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints

TL;DR: A united sampling criterion is proposed to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters to improve the solvability of a problem, convergence properties, and efficiency.
References
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Book

Response Surface Methodology: Process and Product Optimization Using Designed Experiments

TL;DR: Using a practical approach, this book discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques.
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code

TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
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Efficient Global Optimization of Expensive Black-Box Functions

TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.
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Multivariate Adaptive Regression Splines

TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
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The design and analysis of computer experiments

TL;DR: This paper presents a meta-modelling framework for estimating Output from Computer Experiments-Predicting Output from Training Data and Criteria Based Designs for computer Experiments.
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