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相关概念视频

Lossless Lines01:23

Lossless Lines

108
In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi,...
108

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相关实验视频

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Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
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深度学习设计用于优化元材料中的损失.

Xianfeng Wu1, Jing Zhao2, Kunlun Xie1

  • 1Smart Materials Laboratory, Department of Applied Physics, Northwestern Polytechnical University, Xi'an 710129, China.

Nanomaterials (Basel, Switzerland)
|February 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习方法,以最大限度地减少3D可见光元材料中的材料损失. 该方法优化了无序的结构,使强大的,高性能光子设备.

关键词:
深度学习是一种深度学习.没有秩序的分散分散.损失优化 优化 损失优化超材料是一种金属材料.

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 固有的材料损失阻碍了元材料的发展,特别是可见波长的3D结构.
  • 使用贵金属和周期性设计的传统方法已经达到局限性.
  • 制造复杂性和精确对齐是低损耗可见元材料的重大挑战.

研究的目的:

  • 用深度学习和弱相互作用原理重新检查和优化无序的离散超材料.
  • 开发一种创新策略,以优化结构单位分布无序的元材料中的损失.
  • 为设计无序系统中的单频和宽带超材料提供理论框架.

主要方法:

  • 应用深度学习算法来分析和优化超材料结构.
  • 利用弱相互作用的原理来减轻物质损失.
  • 调查无序的离散超材料设计及其分布比.

主要成果:

  • 在具有无序结构单元的元材料中展示了优化损失的强有力的战略.
  • 验证了这些优化的超材料在关键分布比率内执行预期功能的能力.
  • 建立了可见光谱中低损耗元材料的精细设计方法.

结论:

  • 开发的战略为显著减少光学超材料损失提供了一条途径.
  • 这种方法有助于轻松制造高性能光子设备.
  • 它在无序离散系统中推进了单频和宽带超材料的设计原则.