Variable-Fidelity Surrogate

This book delves deeply intothefield ofvariable-fidelity surrogate modeling, examining its application in theoptimization ofcomplex multidisciplinary design optimization problems. Thetext presents a detailed exploration ofsurrogate modeling techniques, witha focus onvariable-fidelity approaches that integrate models ofvarying accuracy toenhance theefficiency ofoptimization processes. Covering foundational concepts, thebook 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 thedevelopment ofsurrogate-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.