Nanophotonics and Machine Learning: Concepts, Fundamentals, and Applications: 241 (Springer Series in Optical Sciences) - Tapa blanda

Acerca del autor

Yuebing 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.

De la contraportada

This 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.