Surrogate Modeling and Optimization Theories, Applications, and Limitations

An expert reference on building surrogate models, using them for optimization, their associated prediction uncertainty, and potential failures, with practical implementation in MATLAB
Surrogate Modeling and Optimizationexplains the meaning of different surrogate models and provides an in-depth understanding of such surrogates, emphasizing how much uncertainty is associated with them, and when and how a surrogate model can fail in approximating complex functions, helping readers understand theory through practical implementation in MATLAB. This book enables readers to obtain an accurate approximate function using as few samples as possible, thereby allowing them to replace expensive computer simulations and experiments during design optimization, sensitivity analysis, and/or uncertainty quantification.
The book is organized into three parts. Part I introduces the basics of surrogate modeling. Part II reviews various theories and algorithms of design optimization. Part III presents advanced topics in surrogate modeling, including the Kriging surrogate, neural network models, multi-fidelity surrogates, and efficient global optimization using Kriging surrogates.
Each chapter contains a multitude of examples and exercise problems. Lecture slides and a solution manual for exercise problems are available for instructors on a companion website.
Topics discussed inSurrogate Modeling and Optimizationinclude:
Various designs of experiments, such as those developed for linear and quadratic polynomial response surfaces (PRS) in a boxlike design spaceCriteria for constrained and unconstrained optimization and the most important optimization theoriesVarious numerical algorithms for gradient-based optimizationGradient-free optimization algorithms, often referred to as global search algorithms, which do not require gradient or Hessian informationDetailed explanations and implementation on Kriging surrogates, often referred to as Gaussian Process, especially when samples include noiseThe combination of a small number of high-fidelity samples with many low-fidelity samples to improve prediction accuracyNeural network models, focusing on training uncertainty and its effect on prediction uncertaintyEfficient global optimization using either polynomial response surfaces or Kriging surrogates
Surrogate Modeling and Optimizationis an essential learning companion for senior-level undergraduate and graduate students in all engineering disciplines, including mechanical, aerospace, civil, biomedical, and electrical engineering. The book is also valuable for industrial practitioners who apply surrogate models to solve their optimization problems.

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