Tutorial EQ08: Artificial Intelligence in Nanophotonics, Metamaterials and Metasurfaces

In recent years, artificial Intelligence has remarkably accelerated the developments of many realms of science and engineering, such as material science, chemistry, physics, quantum mechanics, and computational imaging. Artificial intelligence has also made tremendous advances in the fields of nanophotonics, metamaterials and metasurfaces, allowing efficient forward modeling and inverse design. This tutorial will focus on applications of artificial intelligence in nanophotonics, metamaterials, and metasurfaces. The tutorial will be divided into four parts:

An Overview of On-Demand Design of Metamaterials Enabled by Deep Learning 
Yongmin Liu, Northeastern University

Over the last years, deep learning has been used for on-demand design of metamaterials by representing and learning the mapping between the topology and composition of metamaterials and their associated optical properties. Liu will review the recent progress in on-demand design of metamaterials using deep learning, with an emphasis on various model architectures for specific photonic tasks. He will also provide the historical background, key algorithms, fundamentals, and applications of deep learning used for design of metamaterials.


Decoding Optical Data with Machine Learning 
Yuebing Zheng, The University of Texas at Austin

With the rapid development of various optical spectroscopy and imaging techniques, accurate and rapid analysis of optical data is highly demanding. Meanwhile, machine learning has shown its exceptional capability of decoding complex data over the last decade. Zheng will provide the recent progress in machine learning assisted decoding optical data, which allows accurate analysis of optical data. He will focus on their applications in a wide range of fields.

 
An Overview of Numerical Optimization Methods for Metasurfaces 
Patrice Genevet, Centre de Recherche sur I’Hétéro- Epitaxie et ses Applications

Numerical optimizations search for local or global optimal solutions by either evolutionary or gradient-based approaches. The first method searches solutions stochastically, in an extensive parameter space allowing capture of global optima; the second strategy calculates the derivatives of the cost function and finds local optima. Genevet will review the recent advanced optimization method to further exploit metasurface capabilities. He will also discuss Bayesian-based optimization techniques which can reduce the computational cost substantially.

An Overview of Machine Learning-Assisted Global Optimization of Photonic Devices 
Alexandra Boltasseva, Purdue University

Machine learning has shown great promise for generating data by learning important features. By exploiting its versatility and efficiency, machine-learning assisted novel optimization methods have been reported, coupled with conventional optimization framework including topology optimization and meta-heuristic optimization. In her tutorial, Boltasseva will present the recent progress in global optimization assisted by machine learning, with an emphasis on high efficiency and substantial improvement.