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Predictive and generative machine learning models for photonic crystals.
- Source :
- Nanophotonics (21928606); Oct2020, Vol. 9 Issue 14, p4183-4192, 10p
- Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21928606
- Volume :
- 9
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- Nanophotonics (21928606)
- Publication Type :
- Academic Journal
- Accession number :
- 146225540
- Full Text :
- https://doi.org/10.1515/nanoph-2020-0197