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以物理为驱动的无监督深度学习网络,用于可编程的基于超表面的光束成形.

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此摘要是机器生成的。

我们开发了一个快速的深度学习模型来设计可编程的超表面,用于电磁波操纵. 这种方法快速生成用于束形的数字编码模式,克服了传统的优化挑战.

关键词:
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科学领域:

  • 电磁学 电磁学 电磁学 电磁学
  • 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:

  • 可编程的超表面可以动态操纵电磁 (EM) 波.
  • 设计超表面的数字编码模式对于光束成形至关重要.
  • 编码模式的传统优化方法是计算密集且耗时的.

研究的目的:

  • 提出一种新的物理辅助深度学习 (DL) 模型,用于快速计算数字编码模式.
  • 通过简化设计过程,通过可编程元表面实现高效的梁成型.
  • 为了减少计算复杂性和优化超表面编码模式所需的时间.

主要方法:

  • 开发了一个物理辅助的深度学习模型,集成了一个用于数字编码元表面的扩展宏观模型.
  • 该模型根据编码模式快速计算辐射模式,确保物理符合规则.
  • DL模型从所需的光束描绘中计算出所需的数码编码模式在几毫秒内.

主要成果:

  • 拟议的DL模型成功计算了产生广泛的电磁束的编码模式.
  • 实验验证表明,在单光束和双光束场景中,光束成形性能良好.
  • 该方法显著减少了编码模式生成的时间,从小时到毫秒.

结论:

  • 物理辅助DL模型为设计可编程元表面提供了高效和准确的方法.
  • 这一突破加速了动态电磁波操纵技术的开发和应用.
  • 提出的方法克服了在超表面设计中传统优化算法的局限性.