Deep learning for accelerated all-dielectric metasurface design

Migrating Knowledge between Physical Scenarios Based on Artificial Neural Networks

(2019) Yurui Qu et al.   ACS Photonics

Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

(2018) Wei Ma et al.   ACS Nano

Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures

(2018) Dianjing Liu et al.   ACS Photonics

Valence Electron Density-Dependent Pseudopermittivity for Nonlocal Effects in Optical Properties of Metallic Nanoparticles

(2018) Chao Chen et al.   ACS Photonics

Nanophotonic particle simulation and inverse design using artificial neural networks

(2018) John Peurifoy et al.   Science Advances

Plasmonic nanostructure design and characterization via Deep Learning

(2018) Itzik Malkiel et al.   Light-Science & Applications

Why Does Deep and Cheap Learning Work So Well?

(2017) Henry W. Lin et al.   JOURNAL OF STATISTICAL PHYSICS

Degenerate critical coupling in all-dielectric metasurface absorbers

(2017) Xianshun Ming et al.   OPTICS EXPRESS

Experimental realization of a terahertz all-dielectric metasurface absorber

(2017) Xinyu Liu et al.   OPTICS EXPRESS

All-dielectric metasurface absorbers for uncooled terahertz imaging

(2017) Kebin Fan et al.   Optica

Dielectric Resonator Reflectarray as High-Efficiency Nonuniform Terahertz Metasurface

(2016) Daniel Headland et al.   ACS Photonics

Active Tuning of All-Dielectric Metasurfaces

(2015) Jürgen Sautter et al.   ACS Nano

Deep learning

(2015) Yann LeCun et al.   NATURE

Scalable Nearest Neighbor Algorithms for High Dimensional Data

(2014) Marius Muja et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Taming the Blackbody with Infrared Metamaterials as Selective Thermal Emitters

(2011) Xianliang Liu et al.   PHYSICAL REVIEW LETTERS

Neural Network Inverse Modeling and Applications to Microwave Filter Design

(2008) H. Kabir et al.   IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES