Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
Abstract
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare these results with conventional derivative-free global search algorithms, such as (single-fidelity) Gaussian Processes optimization scheme, and Particle Swarm Optimization---a commonly used method in nanophotonics community, which is implemented in Lumerical commercial photonics software. We demonstrate the performance of various numerical optimization approaches on several pre-collected real-world datasets and show that by properly trading off expensive information sources with cheap simulations, one can more effectively optimize the transmission properties with a fixed budget.
Keywords
Cite
@article{arxiv.1811.07707,
title = {Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes},
author = {Jialin Song and Yury S. Tokpanov and Yuxin Chen and Dagny Fleischman and Kate T. Fountaine and Harry A. Atwater and Yisong Yue},
journal= {arXiv preprint arXiv:1811.07707},
year = {2018}
}
Comments
NIPS 2018 Workshop on Machine Learning for Molecules and Materials. arXiv admin note: substantial text overlap with arXiv:1811.00755