Machine learning approach for computing optical properties of a photonic crystal fiber

Prospects of Photonic Crystal Fiber as Physical Sensor: An Overview

(2019) Moutusi De et al.   SENSORS

An Optical Communication's Perspective on Machine Learning and Its Applications

(2019) Faisal Nadeem Khan et al.   JOURNAL OF LIGHTWAVE TECHNOLOGY

Metasurface-based molecular biosensing aided by artificial intelligence

(2019) Andreas Tittl et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Free-Form Diffractive Metagrating Design Based on Generative Adversarial Networks

(2019) Jiaqi Jiang et al.   ACS Nano

Plasmonic colours predicted by deep learning

(2019) Joshua Baxter et al.   Scientific Reports

Deep learning for accelerated all-dielectric metasurface design

(2019) Christian C. Nadell et al.   OPTICS EXPRESS

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

(2018) Wei Ma et al.   ACS Nano

Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine

(2018) Adriano da Silva Ferreira et al.   JOURNAL OF LIGHTWAVE TECHNOLOGY

End-to-End Deep Learning of Optical Fiber Communications

(2018) Boris Karanov et al.   JOURNAL OF LIGHTWAVE TECHNOLOGY

Learning to see through multimode fibers

(2018) Navid Borhani et al.   Optica

Optimization of photonic crystal nanocavities based on deep learning

(2018) Takashi Asano et al.   OPTICS EXPRESS

All-Normal Dispersion Chalcogenide PCF for Ultraflat Mid-Infrared Supercontinuum Generation

(2017) M. R. Karim et al.   IEEE PHOTONICS TECHNOLOGY LETTERS

Planewave expansion methods for photonic crystal fibres

(2012) R.A. Norton et al.   APPLIED NUMERICAL MATHEMATICS