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可切换数字编码的高效多目标优化,由机器学习赋权的超表面吸收器

Shaowei Zhang, Feng Wen, Fei Wang

|November 11, 2025

概括

本研究介绍了一种高效的多目标优化框架,使用机器学习快速设计先进的多功能超表面吸收器,用于无线安全和通信. 新方法显著提高了设计效率和设备性能.

科学领域:

超材料和纳米光子学
应用电磁学应用电磁学
机器学习应用机器学习应用

背景情况:

超表面吸收器对于无线安全,雷达通信和生物传感至关重要.
传统的设计方法耗时,依赖于经验.
现有的机器学习方法用于超表面吸收器往往是单一目标和低于最佳的.

研究的目的:

开发一个通用和高效的多目标优化框架,用于设计多功能超表面吸收器.
克服传统和现有的基于机器学习的设计方法的局限性.
为了加快复杂的超表面设备的设计过程.

主要方法:

使用非主导排序遗传算法II (NSGA-II),一种机器学习算法.
设计了一个四通道双层多功能数字编码的超表面吸收器.
整合了九种可编程编码模式与四个数字逻辑开关,以实现无过渡.

主要成果:

该框架有效地处理同质和异质的多目标优化问题.
在10代内实现了快速融合,超过了传统的遗传算法.
优化的设备显示了减少的FWHM,优异的阻抗匹配,简单的制造,角宽容和极化不敏感.

结论:

拟议的框架在设计多功能地表吸收器方面取得了重大进展.
优化的设备在多种操作模式中表现出卓越的性能特征.
这种方法有可能用于设计无色金属透镜,传感器和探测器.

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