Journal ArticleDOI
Review of Metamodeling Techniques in Support of Engineering Design Optimization
Gongming Wang,Songqing Shan +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 ASMEread more
Citations
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Analysis of Approximation-Based Memetic Algorithms for Engineering Optimization
TL;DR: This chapter discusses the treatment of expensive optimization problems in Computer-Aided Design (CAD) problems by combining two strategies: first, the whole optimization varying the accuracy with which a given candidate solution is evaluated by the expensive black-box function, rather than using the same accuracy for all evaluations.
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QUICKER: Quantifying Uncertainty In Computational Knowledge Engineering Rapidly—A rapid methodology for uncertainty analysis
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Efficient Batch Black-box Optimization with Deterministic Regret Bounds.
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