Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Metagrating Design based on Reinforcement Learning

Not Accessible

Your library or personal account may give you access

Abstract

We demonstrate an algorithm based on reinforcement learning to realize high efficiency multifunctional metagrating devices without the requirement of a training dataset.

© 2023 The Author(s)

PDF Article
More Like This
A Universal Approach to Nanophotonic Inverse Design through Reinforcement Learning

Marco Butz, Alexander Leifhelm, Marlon Becker, Benjamin Risse, and Carsten Schuck
STh4G.3 CLEO: Science and Innovations (CLEO:S&I) 2023

Reinforcement-Learning-based Multilayer Path Planning Framework that Designs Grooming, Route, Spectrum, and Operational Mode

Takafumi Tanaka and Katsuaki Higashimori
We5.59 European Conference and Exhibition on Optical Communication (ECOC) 2022

Reinforcement-Learning-based Network Design and Control with Stepwise Reward Variation and Link-Adjacency Embedding

Kenji Cruzado, Ryuta Shiraki, Yojiro Mori, Takafumi Tanaka, Katsuaki Higashimori, Fumikazu Inuzuka, Takuya Ohara, and Hiroshi Hasegawa
We2B.3 European Conference and Exhibition on Optical Communication (ECOC) 2022

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.