Predictive and generative machine learning models for photonic crystals
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 29, 2020
- Source ID
- 10.1515/nanoph-2020-0197
Entities
People
- Charlotte Loh
- Domagoj Jakobović
- Jing Li
- John D. Joannopoulos
- Marin Soljačić
- Sophie Fisher
- Stjepan Picek
- Thomas Christensen
- Vladimir Čeperić
Organizations
- Army Research Office
- Defense Advanced Research Projects Agency
- Delft University of Technology
- Massachusetts Institute of Technology
- National Science Foundation
- Nvidia
- University of Zagreb