Abstract

The primary topic of the book is surrogate modeling and surrogate-based design of high-frequency structures. The purpose of the first two chapters is to provide the reader with an overview of the two most important classes of modeling methods, data-driven (or approx-imation), as well as physics-based ones. These are covered in Chap-ters 1 and 2, respectively. The remaining parts of the book give an exposition of the specific aspects of particular modeling methodolo-gies and their applications to solving various simulation-driven de-sign tasks such as parametric optimization or uncertainty quantifica-tion. Data-driven models are by far the most popular types of surro-gates. This is due to several reasons, including versatility, low evalu-ation cost, a large variety of matured methods, and—important from the point of view of practical utility—widespread availability through third-party toolboxes implemented in programming envi-ronments such as Matlab. This chapter covers the fundamentals of approximation-based modeling. We discuss the surrogate modeling flow, design of experiments, selected modeling methods (e.g., kriging, radial basis functions, support vector regression, or polyno-mial chaos expansion), as well as discuss model validation ap-proaches. The presented material is intended to provide the readers who are new to the subject with the basics necessary to understand the remaining parts of the book. On the other hand, it is by no means exhaustive, and the readers interested in a more detailed exposition can refer to a rich literature of the subject.

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