Introduction to Surrogate Modeling and Surrogate-Based Optimization

Slawomir Koziel and Leifur Leifsson
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Slawomir Koziel: Reykjavik University, Engineering Optimization & Modeling Center
Leifur Leifsson: Iowa State University, Department of Aerospace Engineering

Chapter Chapter 4 in Simulation-Driven Design by Knowledge-Based Response Correction Techniques, 2016, pp 31-61 from Springer

Abstract: Abstract Surrogate-based optimization (SBO) is the main focus of this book. We provide a brief introduction to the subject in this chapter. In particular, we recall the SBO concept and the optimization flow, discuss the principles of surrogate modeling and typical approaches to construct surrogate models. We also discuss the distinction between function approximation (or data-driven) surrogates and physics-based surrogates, as well as outline the algorithm of SBO exploiting the two aforementioned classes of models. More detailed information about the selected types of SBO algorithms (especially those involving response correction techniques) as well as illustration and application examples in various fields of engineering are provided in the remaining part of the book.

Keywords: Design Space; Support Vector Regression; Gaussian Process Regression; Efficient Global Optimization; Response Surface Approximation (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-30115-0_4

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