Comparison of algorithms using deep reinforcement learning for optimization of hyperbolic metamaterials
Abstract A hyperbolic metamaterial absorber has great potential for improving the performance of photo-thermoelectric devices targeting heat sources owing to its broadband absorption. However, optimizing its geometry requires considering numerous parameters to achieve absorption that aligns with the...
Saved in:
Main Authors: | Kenta Hamada, Hui-Hsin Hsiao, Wakana Kubo |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-12-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-024-83167-z |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An ultra-wideband thin metamaterial linear cross-polarization conversion
by: Pegah Nochian, et al.
Published: (2025-01-01) -
Near‐Isotropic, Extreme‐Stiffness, Continuous 3D Mechanical Metamaterial Sequences Using Implicit Neural Representation
by: Yunkai Zhao, et al.
Published: (2025-01-01) -
Investigating the compressive behavior of zeolite-based porous mechanical metamaterials
by: Dosung Lee, et al.
Published: (2025-01-01) -
Excellent mechanical properties of a novel double-diagonal reinforced mechanical metamaterial with tunable Poisson’s ratios inspired by deep-sea glass sponges
by: Hongbo Zhang, et al.
Published: (2025-02-01) -
Metacavities by harnessing the linear-crossing metamaterials
by: Wu Jiaju, et al.
Published: (2025-01-01)