Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > CLEO_AT > 2022 > JW3A > JW3A.8

Use of neural networks fro designing robust flat-optics on flexible substrates

F. Getman, M. Makarenko, Q. Wang, A. Burguete-Lopez, and A. Fratalocchi

Not Accessible

Your library or personal account may give you access

Abstract

We present an inverse design platform that enables the fast design of flexible flat-optics that maintain high performance under deformations. The platform is based on evolutionary large-scale optimizers, and neural network predictors.

© 2022 The Author(s)

PDF Article
More Like This
Robust Flat Optics for Broadband Light Control on Flexible Substrates

Arturo Burguete-Lopez, Maksim Makarenko, Qizhou Wang, Fedor Getman, and Andrea Fratalocchi
NoM4C.3 Novel Optical Materials and Applications (NOMA) 2021

Experimental Demonstration of Deformation Robust Flexible Flat Optics for the Visible

Arturo Burguete-Lopez, Maksim Makarenko, Qizhou Wang, Fedor Getman, and Andrea Fratalocchi
CMP16B_04 Conference on Lasers and Electro-Optics/Pacific Rim (CLEO/PR) 2022

Multiplane light conversion design with physical neural network

Zheyuan Zhu, Joe H. Doerr, Guifang Li, and Sean Pang
M6A.2 Digital Holography and Three-Dimensional Imaging (DH) 2022

View More...

Poster Presentation

Media 1: PDF (23295 KB)     
Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use