Naslov
Predictive and generative machine learning models for photonic crystals

Autori
Christensen, Thomas ; Loh, Charlotte ; Picek, Stjepan ; Jakobović, Domagoj ; Jing, Li ; Fisher, Sophie ; Ceperic, Vladimir ; Joannopoulos, John ; Soljačić, Marin

Izvornik
Nanophotonics (2192-8606) 9 (2020), 13; 4183-4192

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
photonic crystals ; machine learning ; generative models

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA