Nanophotonics and machine learning : concepts, fundamentals, and applications
著者
書誌事項
Nanophotonics and machine learning : concepts, fundamentals, and applications
（Springer series in optical sciences, v. 241）
Springer, c2023
大学図書館所蔵 件 / 全4件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Tentative 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
Index
「Nielsen BookData」 より
