Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes

Autor:	Song, Jialin, Tokpanov, Yury S., Chen, Yuxin, Fleischman, Dagny, Fountaine, Kate T., Atwater, Harry A., Yue, Yisong
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: NIPS 2018 Workshop on Machine Learning for Molecules and Materials. arXiv admin note: substantial text overlap with arXiv:1811.00755
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.07707 Zobrazit plný text záznamu View this record from Arxiv