Surrogate Modeling and Optimization

Surrogate Modeling and Optimization, 1st Edition, provides a reference focused on constructing surrogate models and applying them to design optimization. Authored by Nam-Ho Kim, this work addresses the requirement in advanced engineering programs for systematic model approximation techniques. The publication is structured for senior-level undergraduate students, graduate students in engineering, and industrial practitioners who work with complex engineering design problems.

The material progresses from foundational principles of surrogate modeling in Part I to specialized methodologies in Part III. Advanced subjects in Part III encompass Kriging surrogates, neural networks, multi-fidelity modeling, and efficient global optimization algorithms. In discussing neural network models, the text emphasizes training uncertainty and its direct effect on prediction uncertainty during evaluation.

To connect conceptual framework to executable practice, the volume pairs theoretical concepts with practical implementation tools in MATLAB. Every chapter includes worked example problems alongside practice exercise problems designed to support student learning and professional application.