Deep learning in nano-photonics: inverse design and beyond

PR Wiecha, A Arbouet, C Girard, OL Muskens - Photonics Research, 2021 - opg.optica.org
Deep learning in the context of nano-photonics is mostly discussed in terms of its potential
for inverse design of photonic devices or nano-structures. Many of the recent works on …

Deep learning the electromagnetic properties of metamaterials—a comprehensive review

O Khatib, S Ren, J Malof… - Advanced Functional …, 2021 - Wiley Online Library
Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …

Physics-informed machine learning: A survey on problems, methods and applications

Z Hao, S Liu, Y Zhang, C Ying, Y Feng, H Su… - ar** virtual reality
Z Li, R Pestourie, JS Park, YW Huang… - Nature …, 2022 - nature.com
Meta-optics has achieved major breakthroughs in the past decade; however, conventional
forward design faces challenges as functionality complexity and device size scale up …

[HTML][HTML] Roadmap on photonic metasurfaces

SA Schulz, R Oulton, M Kenney, A Alù, I Staude… - Applied Physics …, 2024 - pubs.aip.org
Here we present a roadmap on Photonic metasurfaces. This document consists of a number
of perspective articles on different applications, challenge areas or technologies underlying …

Intelligent designs in nanophotonics: from optimization towards inverse creation

N Wang, W Yan, Y Qu, S Ma, SZ Li, M Qiu - PhotoniX, 2021 - Springer
Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing
and extremely active scientific topic over the past few years. Inverse design of functional …

A newcomer's guide to deep learning for inverse design in nano-photonics

A Khaireh-Walieh, D Langevin, P Bennet, O Teytaud… - …, 2023 - degruyter.com
Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as
light concentration, routing, and filtering. Designing these devices to achieve precise light …

Neural operator-based surrogate solver for free-form electromagnetic inverse design

Y Augenstein, T Repan, C Rockstuhl - ACS Photonics, 2023 - ACS Publications
Neural operators have emerged as a powerful tool for solving partial differential equations in
the context of scientific machine learning. Here, we implement and train a modified Fourier …