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OverviewThis book, the first of its kind, bridges the gap between the increasingly interlinked fields of nanophotonics and artificial intelligence (AI). While artificial intelligence techniques, machine learning in particular, have revolutionized many different areas of scientific research, nanophotonics holds a special position as it simultaneously benefits from AI-assisted device design whilst providing novel computing platforms for AI. This book is aimed at both researchers in nanophotonics who want to utilize AI techniques and researchers in the computing community in search of new photonics-based hardware. The book guides the reader through the general concepts and specific topics of relevance from both nanophotonics and AI, including optical antennas, metamaterials, metasurfaces, and other photonic devices on the one hand, and different machine learning paradigms and deep learning algorithms on the other. It goes on to comprehensively survey inverse techniques for device design, AI-enabled applications in nanophotonics, and nanophotonic platforms for AI. This book will be essential reading for graduate students, academic researchers, and industry professionals from either side of this fast-developing, interdisciplinary field. Full Product DetailsAuthor: Kan Yao , Yuebing ZhengPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 1st ed. 2023 Volume: 241 Weight: 0.459kg ISBN: 9783031204722ISBN 10: 3031204727 Pages: 178 Publication Date: 28 March 2023 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsTentative Title: Nanophotonics and Machine Learning: Concepts, Fundamentals, and Applications Introduction Chapter 1 Fundamentals of Nanophotonics 1.1 Surface Plasmon Polaritons 1.2 Metamaterials and Metasurfaces 1.3 Mie Scattering 1.4 Optical Antennas 1.5 Integrated Photonics 1.6 Miscellaneous (chirality, solar cells, etc. optional) References Chapter 2 Optimization Techniques for Inverse Design 2.1 Adjoint-Based Simulation 2.2 Topological Optimization 2.3 Genetic Algorithms References Chapter 3 Fundamentals of Artificial Intelligence 3.1 Classification of AI 3.2 Learning and Artificial Neural Networks 3.3 Convolutional Neural Network 3.4 Generative Adversarial Networks 3.5 Reinforcement Learning 3.6 Miscellaneous (some non-deep-learning models, optional) References Chapter 4 AI-Assisted Inverse Design in Nanophotonics 4.1 Metasurfaces with Arbitrary Transmission/Reflection/Absorption Properties 4.2 Metasurfaces for Beam Steering and Polarization control 4.3 Metasurfaces for Thermal Management 4.4 Chiral Metamaterials 4.5 Controlling Scattering Properties of Nanostructures 4.6 Classification of Photonic Modes References Chapter 5 AI-enabled Applications in Nanophotonics 5.1 Knowledge Discovery and Migration 5.2 Predictors for Vectorial Fields References Chapter 6 Nanophotonic Platforms for AI 6.1 Neural Networks Based on Diffractive Optics 6.2 Artificial Neural Inference Using Scattering Media 6.3 Deep Learning with Nanophotonic Circuits 6.4 Training of Photonic Neural Networks References Chapter 7 Concluding Remarks and Outlook References IndexReviewsAuthor InformationYuebing Zheng: Yuebing Zheng is an Associate Professor of Mechanical Engineering and Materials Science & Engineering at the University of Texas at Austin, USA, directing Zheng Research Group. He is holding the Temple Foundation Endowed Teaching Fellowship in Engineering #2. Yuebing received his Ph.D. in Engineering Science and Mechanics (with Prof. Tony Jun Huang) from the Pennsylvania State University, USA, in 2010. He was a postdoctoral researcher in Chemistry and Biochemistry (with Prof. Paul S. Weiss) at the University of California, Los Angeles from 2010 to 2013. His research group innovates optical manipulation and measurement for biological and nanoscale world. He received University Co-op Research Excellence Award for Best Paper, Materials Today Rising Star Award, NIH Director’s New Innovator Award, NASA Early Career Faculty Award, ONR Young Investigator Award, and Beckman Young Investigator Award. Kan Yao is currently a postdoctoral fellow in the University of Texas at Austin. He received his PhD degree in Electrical Engineering in 2017 from Northeastern University (Boston, USA), where he worked with Prof. Yongmin Liu. Before the enrollment in a PhD program, he spent 3 years in Chinese Academy of Sciences as a research assistant and in Soochow University (Suzhou, China) as a visiting scholar. Kan obtained bachelor’s and master’s degrees from the University of Science and Technology of China (2006) and Chinese Academy of Sciences (2009), respectively. His research interests include nanophotonics, plasmonics, metamaterials and metasurfaces, light-matter interactions, transformation optics, and other topics concerning field/wave phenomena. Tab Content 6Author Website:Countries AvailableAll regions |
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