This book delves deeply into the field of variable-fidelity surrogate modeling, examining its application in the optimization of complex multidisciplinary design optimization problems. The text presents a detailed exploration of surrogate modeling techniques, with a focus on variable-fidelity approaches that integrate models of varying accuracy to enhance the efficiency of optimization processes. Covering foundational concepts, the book progresses through diverse modeling strategies, including scaling function-based approaches, sequential techniques, physics-informed neural networks-based and deep transfer learning-based methods. It also addresses critical aspects such as the development of surrogate-assisted optimization algorithms.

By adopting a holistic perspective, this book emphasizes the importance of integrating surrogate models with optimization algorithms to tackle real-world multidisciplinary design challenges. The book is  designed for graduate students, researchers, and engineers working in areas such as engineering design, optimization, and computational modeling. It is an essential resource for those interested in advancing the field of surrogate modeling and its applications to complex design optimization tasks, providing both theoretical insights and practical guidance.