Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning
Optics
2020-02-19 v1 Machine Learning
Abstract
By learning the optimal policy with a double deep Q-learning network, we design ultra-broadband, biomimetic, perfect absorbers with various materials, based the structure of a moths eye. All absorbers achieve over 90% average absorption from 400 to 1,600 nm. By training a DDQN with motheye structures made up of chromium, we transfer the learned knowledge to other, similar materials to quickly and efficiently find the optimal parameters from the around 1 billion possible options. The knowledge learned from previous optimisations helps the network to find the best solution for a new material in fewer steps, dramatically increasing the efficiency of finding designs with ultra-broadband absorption.
Keywords
Cite
@article{arxiv.1910.12465,
title = {Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning},
author = {Trevon Badloe and Inki Kim and Junsuk Rho},
journal= {arXiv preprint arXiv:1910.12465},
year = {2020}
}