Deep Learning Design for Loss Optimization in Metamaterials
Inherent material loss is a pivotal challenge that impedes the development of metamaterial properties, particularly in the context of 3D metamaterials operating at visible wavelengths. Traditional approaches, such as the design of periodic model structures and the selection of noble metals, have enc...
Saved in:
| Main Authors: | Xianfeng Wu, Jing Zhao, Kunlun Xie, Xiaopeng Zhao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Nanomaterials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-4991/15/3/178 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Spin Hall Effect Enhancement of Transmitted Light Through an Anisotropic Metamaterial Slab
by: Tingting Tang, et al.
Published: (2017-01-01) -
Comparison of algorithms using deep reinforcement learning for optimization of hyperbolic metamaterials
by: Kenta Hamada, et al.
Published: (2024-12-01) -
Spring-based mechanical metamaterials with deep-learning-accelerated design
by: Xiaofeng Guo, et al.
Published: (2025-04-01) -
Dispersion Properties of Surface Polaritons in the Ferrite/Semiconductor Metamaterial in the Magnetic Field
by: I.V. Fedorin, et al.
Published: (2016-11-01) -
Comparative optical loss analysis of multilayer type-I hyperbolic metamaterials
by: Kadir Üstün, et al.
Published: (2025-08-01)