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Surrogate Modeling and Optimization Using Shape-Preserving Response Prediction: A Review

Abstract

Computer simulation models are ubiquitous in modern engineering design. In many cases, they are the only way to evaluate a given design with sufficient fidelity. Unfortunately, an added computa-tional expense is associated with higher fidelity models. Moreover, the systems being considered are often highly nonlinear and may feature a large number of designable parameters. Therefore, it may be impractical to solve the design problem with conventional optimization algorithms. A promising approach to alleviate these difficulties is surrogate-based optimization (SBO). Among proven SBO techniques, the methods utilizing surrogates constructed from corrected physics-based low-fidelity models are, in many cases, the most efficient. In this paper, we review a particular technique of this type, namely, the shape-preserving response prediction (SPRP) technique, which works on the level of the model responses to correct the underlying low-fidelity models. The for-mulation and limitations of SPRP are discussed. Applications to several engineering design prob-lems are provided.

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Category:
Articles
Type:
artykuł w czasopiśmie wyróżnionym w JCR
Published in:
ENGINEERING OPTIMIZATION no. 48, edition 3, pages 476 - 496,
ISSN: 0305-215X
Language:
English
Publication year:
2016
Bibliographic description:
Leifsson L., Kozieł S.: Surrogate Modeling and Optimization Using Shape-Preserving Response Prediction: A Review// ENGINEERING OPTIMIZATION. -Vol. 48, iss. 3 (2016), s.476-496
DOI:
Digital Object Identifier (open in new tab) 10.1080/0305215x.2015.1016509
Verified by:
Gdańsk University of Technology

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