Abstract
Abstract
Optimization of the geometry of broadband metamaterial absorbers is crucial for improving the performance of optoelectronic devices. However, a large number of geometric parameters should be considered to achieve broad absorption, which is time-consuming. Herein, we propose a rapid and simple method for optimizing metamaterial absorbers dedicated to thermal radiation absorption using deep reinforcement learning. Deep reinforcement learning generated an ideal geometry for a broadband metamaterial absorber after 4 h, demonstrating the effectiveness of this technique for the rapid and effective optimization of metamaterial absorbers.
Funder
Japan Society for the Promotion of Science
Thermal and Electric Energy Technology Foundation
Subject
General Physics and Astronomy,General Engineering