Variable-Fidelity Surrogate : Experiment Design, Modeling, and Applications on Design Optimization (Engineering Applications of Computational Methods)

個数：

予約

Variable-Fidelity Surrogate : Experiment Design, Modeling, and Applications on Design Optimization (Engineering Applications of Computational Methods)

Yi, Jin/ Zheng, Jun/ Li, Xinyu

ウェブストア価格 ¥43,645（本体¥39,678）
Springer Verlag, Singapore（2026/02発売）
外貨定価 US$ 199.99
ポイント 396pt

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製本 Hardcover:ハードカバー版／ページ数 190 p.
言語 ENG
商品コード 9789819555260

Full Description

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.

Preface.- Chapter 1 Introduction.- Chapter 2 Key Technologies for Surrogate Modeling.- Chapter 3 Fast Nested Latin Hypercube Design via Translation Propagation.- Chapter 4 Nested Maximin Designs Based on Successive Local Enumeration and Discrete Optimization.- Chapter 5 Variable-Fidelity Surrogate Modeling via Scale Functions.- Chapter 6 Variable-Fidelity Physics-Informed Neural Networks.- Chapter 7 Multi-Fidelity Transfer Learning Model Based on Dynamic Task-Weighted Loss.- Chapter 8 Online Variable-Fidelity Surrogate-Assisted Harmony Search Algorithm with Multi-Level Screening Strategy.- Chapter 9 Expensive Design Optimization With Transfer-Learning Based Sequential Variable-Fidelity Surrogate.- Chapter 10 Conclusion Remarks.