Multi-fidelity Surrogates: Engineering Applications of Computational Methods, cartea 12
Autor Qi Zhou, Min Zhao, Jiexiang Hu, Mengying Maen Limba Engleză Hardback – 8 noi 2022
This book investigates two types of static multi-fidelity surrogates modeling approaches, sequential multi-fidelity surrogates modeling approaches, the multi-fidelity surrogates-assisted efficient global optimization, reliability analysis, robust design optimization, and evolutionary optimization. Multi-fidelity surrogates have attracted a significant amount of attention in simulation-based design and optimization in recent years. Some real-life engineering design problems, such as prediction of angular distortion in the laser welding, optimization design of micro-aerial vehicle fuselage, and optimization design of metamaterial vibration isolator, are also provided to illustrate the ability and merits of multi-fidelity surrogates in support of engineering design. Specifically, lots of illustrative examples are adopted throughout the book to help explain the approaches in a more “hands-on” manner. This book is a useful reference for postgraduates and researchers of mechanical engineering, as well as engineers of enterprises in related fields.
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Specificații
ISBN-13: 9789811972096
ISBN-10: 9811972095
Pagini: 464
Ilustrații: VIII, 456 p. 263 illus., 225 illus. in color.
Dimensiuni: 160 x 241 x 29 mm
Greutate: 0.89 kg
Ediția:1st ed. 2023
Editura: Springer
Colecția Engineering Applications of Computational Methods
Seria Engineering Applications of Computational Methods
Locul publicării:Singapore, Singapore
ISBN-10: 9811972095
Pagini: 464
Ilustrații: VIII, 456 p. 263 illus., 225 illus. in color.
Dimensiuni: 160 x 241 x 29 mm
Greutate: 0.89 kg
Ediția:1st ed. 2023
Editura: Springer
Colecția Engineering Applications of Computational Methods
Seria Engineering Applications of Computational Methods
Locul publicării:Singapore, Singapore
Cuprins
Preface.- Chapter 1 Introduction.- Chapter 2 Hierarchical multi-fidelity surrogates modeling.- Chapter 3 Non-Hierarchical multi-fidelity surrogates modeling.- Chapter 4 Sequential multi-fidelity surrogates modeling.- Chapter 5 Multi-fidelity surrogates assisted efficient global optimization.- Chapter 6 Multi-fidelity surrogates assisted reliability design optimization.- Chapter 7 Multi-fidelity surrogates assisted robust design optimization.- Chapter 8 Multi-fidelity surrogates assisted evolutional optimization.- Chapter 9 Engineering Applications.- Chapter 10 Concluding remarks.
Notă biografică
Liang Cao received received the B.S. degree in mathematics and applied mathematics from Baise University, Baise, China, in 2013, the M.S. degree in computational mathematics from Guangxi University for Nationalities, Nanning, China, in 2016, and the Ph.D degree in Control Science and Engineering in Guangdong University of Technology, Guangzhou, China, in 2019. From 2020 to 2022, he was a Postdoctoral Researcher with the School of Automation, Guangdong University of Technology, Guangzhou, China. He is currently a professor at the college of Mathematical Sciences, Bohai University, Jinzhou, China. His current research interests include event-triggered control, intelligent control and adaptive control for nonlinear systems.
Dr. Cao was the recipient of the Outstanding Reviewer Award of the Outstanding Reviewer Award of Asian Journal of Control 2023. Dr. Cao was a recipient of the Excellent Oral Presentation Award from ICCR 2022.
Yingnan Pan received the B.S. degree in mathematics and applied mathematics and the M.S. degree in applied mathematics from Bohai University, Jinzhou, China, in 2012 and 2015, respectively, and the Ph.D. degree in navigation guidance and control from Northeastern University, Shenyang, China, in 2019. He is currently a professor with Bohai University, Jinzhou, China. His current research interests include fuzzy control, adaptive control, event-triggered control and their applications.
Dr. Pan was the recipient of the Outstanding Reviewer Award of Neurocompting 2016 and the Outstanding Reviewer Award of Asian Journal of Control 2021. Dr. Pan was a recipient of the Best Paper Award from YAC 2020. He has been in the Editorial Board of Automation, Control and Intelligent Systems.
Hongjing Liang received the B.S. degree in mathematics from Bohai University, Jinzhou, China, in 2009, and the M.S. degree in fundamental mathematics and the Ph.D. degree in control theory and control engineering in 2016, both from Northeastern University, Shenyang, China. He was a Temporary Research Associate with the Science Program, Texas A\&M University, Doha, Qatar. He is currently a Professor with the University of Electronic Science and Technology of China, Chengdu, China. His current research interests include adaptive control, fuzzy control, and cooperative control and their applications.
Dr. Liang was the recipient of the Outstanding Reviewer Award of CAA/Automatica Sinica 2019. He has been in the Editorial Board of IEEE TRANSACTIONS ON SYSTEMS,MAN AND CYBERNETICS: SYSTEMS, International Journal of Fuzzy Systems, Fluctuation and Noise Letters.
Qi Zhou received the B.S. and M.S. degrees in mathematics from Bohai University, Jinzhou, China, in 2006 and 2009, respectively, and the Ph.D. degree in control science and engineering from the Nanjing University of Science and Technology, Nanjing, China, in 2013. She was a Research Associate with the the School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia. She is currently a professor with the Southwest University, Chongqing, China.
Her research interests include adaptive control, intelligent control, and their applications. Prof. Zhou was a recipient of the 2016 and 2019 Andrew P. Sage Best Transactions Paper Awards from IEEE System, Man, Cybernetics Society, the Best Paper Award in Theory from ICCSS 2017, and the Zadeh Best Student Paper from IEEE ICCSS 2019. She has been on the editorial boards of several international journals, including IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, IEEE TRANSACTIONS ON FUZZY SYSTEMS, International Journal of Fuzzy Systems, IEEE Access and Neurocomputing.
Textul de pe ultima copertă
This book investigates two types of static multi-fidelity surrogates modeling approaches, sequential multi-fidelity surrogates modeling approaches, the multi-fidelity surrogates-assisted efficient global optimization, reliability analysis, robust design optimization, and evolutionary optimization. Multi-fidelity surrogates have attracted a significant amount of attention in simulation-based design and optimization in recent years. Some real-life engineering design problems, such as prediction of angular distortion in the laser welding, optimization design of micro-aerial vehicle fuselage, and optimization design of metamaterial vibration isolator, are also provided to illustrate the ability and merits of multi-fidelity surrogates in support of engineering design. Specifically, lots of illustrative examples are adopted throughout the book to help explain the approaches in a more “hands-on” manner. This book is a useful reference for postgraduates and researchers of mechanical engineering, as well as engineers of enterprises in related fields.
Caracteristici
Offers an in-depth review of modeling, optimization, and applications of multi-fidelity surrogates Provides multi-fidelity surrogates-assisted design optimization under both deterministic and uncertainty Includess numerical cases to describe each multi-fidelity surrogate method steps- by -steps