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利用缺失的光谱相关性进行表层反向设计

Jie Zhang^1,2,3, Chao Qian^1,2,3, Guangfeng You^1,2,3

¹ZJU-UIUC Institute, Interdisciplinary Center for Quantum Information, State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University, Hangzhou, 310027, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|July 1, 2024

概括

这项研究将物理定律,特别是克拉默斯-克罗尼格关系,整合到电磁超表面设计的深度学习中. 这种方法提高了准确性,并为神经网络性能提供了可解释的见解.

科学领域:

物理物理学物理
计算机科学计算机科学
材料科学材料科学材料科学

背景情况:

对于电磁超表面的深度学习模型通常充当"黑子",缺乏物理解释性.
现有的光子学智能应用忽视了基本的物理定律,阻碍了性能和理解.
由克拉默斯-克罗尼格关系支配的真实和虚构部分之间的内在光谱连接在深度学习中仍然在很大程度上未被开发.

研究的目的:

揭示和利用Kramers-Kronig关系在深度学习框架内进行超表面反向设计.
通过结合物理光谱连接来增强神经网络的特征提取能力.
为各种神经网络架构的性能差异提供物理可解释的见解.

主要方法:

利用克拉默斯-克罗尼格关系来建立光谱元件之间的内在相关性.
在神经网络空间中实现双向信息流,以模拟这些物理关系.
基于双向循环神经网络与完全连接的网络,单向循环神经网络和基于注意力的变压器进行了对比,以实现超表面反向设计.

主要成果:

与其他架构相比,双向循环神经网络在地表反向设计中表现出更好的准确性.
对中间网络产品的分析为跨不同网络结构的性能变化提供了物理解释.
物理定律的整合提高了关键特征提取的有效性.

结论:

将物理定律 (如克拉默斯-克罗尼格关系) 纳入深度学习中,为超表面设计提供了更容易解释和更有效的方法.
神经网络中的双向信息流可以成功模仿物理光谱连接,提高模型性能.
这项工作为开发数据密集型研究努力提供了一条途径,以更大的物理洞察力和透明度.

关键词:

双向神经网络是一种双向的神经网络.设计的反向设计.metasurface 地表的表面是什么物理关系是物理关系.

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