Multi-fidelity Surrogates

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.

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

Autorentext

Shuhao Fan received her B.Sc. in Electronic and Information Engineering from Northeast Forestry University, China, in 2016, and her M.Sc. in IC Design Engineering from Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2017. She earned her Ph.D. in Electrical and Computer Engineering from the University of Macau, Macau, in 2024. Her research focuses on analog CMOS circuit design for portable nuclear magnetic resonance (NMR) systems and miniaturized magnetic resonance imaging (MRI) platforms. Qi Zhou received the B.Sc. degree in instrument science and engineering from Southeast University, Nanjing, China, in 2017, and the M.Sc. degree in electronics and communications engineering from Sun Yat-sen University, Guangzhou, China, in 2019. He is currently pursuing the Ph.D. degree in electrical and computer engineering (ECE) with the University of Macau, Macau. His current research interests include portable nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI) systems and related applications. Ka-Meng Lei received the B.Sc. degree in electrical and electronic engineering (EEE) and the Ph.D. degree in electrical and computer engineering (ECE) from the University of Macau, Macau, in 2012 and 2016, respectively. He has served as an Assistant professor at the University of Macau since 2019. He was a Postdoctoral Fellow at Harvard University from 2017 to 2019, where he was involved in developing the high-resolution portable nuclear magnetic resonance (NMR) spectrometer. Ka-Meng Lei has published 40+ refereed papers. He co-authored one book Handheld Total Chemical and Biological Analysis Systems: Bridging NMR, Digital Microfluidics, and Semiconductors (Springer’18), and two book chapter Micro-NMR on CMOS for Biomolecular Sensing (Springer’18) and Ultra-Low-Voltage Clock References (Springer’23). His current research interests include ultralow voltage analog circuit techniques, sensors and analog front-end interfaces, and high-resolution portable NMR platforms. Rui P. Martins received the Ph.D. in Electrical Engineering and Computers from the Department of Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon, Portugal, in 1992, and has been with that Department since October 1980. From 1992, he has been on leave from University of Lisbon and with the Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macao, China, where he is a Chair-Professor since Aug. 2013. His research interests are on analog and mixed-signal VLSI design and has authored or co-authored 900+ publications, including 10 books, 12 book chapters, 50 Patents, 300+ papers in scientific journals and 400+ in conference proceedings. He was the Founding Director of the State Key Laboratory of Analog and Mixed-Signal VLSI, between 2011 and 2022, and is currently the Director of the Institute of Microelectronics, both at the University of Macau, Macao, China. Prof. Rui Martins is an IEEE Fellow, received an Author Recognition Award at the 70 years of ISSCC, in 2023, as a Top Contributor with more than 50 papers, and 3 Medals from Macao Government in 1999, 2001 and 2021. Since July 2010 he is an Academician with the Lisbon Academy of Sciences, Portugal. Pui-In Mak received the Ph.D. degree from University of Macau (UM), Macao, China, in 2006. He is currently Professor at UM Faculty of Science and Technology – ECE Department, Director at the State Key Laboratory of Analog and Mixed-Signal VLSI and Deputy Director (Research) at the Institute of Microelectronics. His research interests are on analog and radio-frequency (RF) circuits and systems for wireless and multidisciplinary innovations. His involvements with IEEE are: Editorial Board Member of IEEE Press (’14-’16); Member of Board-of-Governors of IEEE Circuits and Systems Society (’09-’11); Senior Editor of IEEE

Klappentext

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.

Inhalt

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. <p