Predictive and generative machine learning models for photonic crystals
Name
[21928614 - Nanophotonics] Predictive and generative machine learning models for photonic crystals.pdf
Description
Published version
Size
2.01 MB
Format
Unknown
Checksum (MD5)
efbd7e254c3faaeecf00d56ff17e865c
Author(s)
Christensen, Thomas
Loh, Charlotte
Picek, Stjepan
Jakobović, Domagoj
Jing, Li
Fisher, Sophie
Ceperic, Vladimir
Joannopoulos, John D.
Soljačić, Marin
Date Issued
June 2020
Journal
Nanophotonics
Publisher
Walter de Gruyter GmbH
Version
Final published version
Abstract
© 2020 Thomas Christensen et al., published by De Gruyter, Berlin/Boston 2020. The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1515/nanoph-2020-0197
