论文标题
限制的串联神经网络辅助跨度的微波吸收的逆设计
Constrained tandem neural network assisted inverse design of metasurfaces for microwave absorption
论文作者
论文摘要
在科学和工程社区中，使用自定义频谱设计微波吸收器是一个有吸引力的话题。但是，由于涉及大量的设计参数，设计过程通常是耗时的，计算量昂贵。为了应对这一挑战，机器学习已成为优化设计参数的强大工具。在这项工作中，我们提出了一个由多层元面组成的吸收器的分析模型，并提出了一种基于受约束的串联神经网络的新型逆设计方法。该网络可以为给定的吸收光谱提供优化的结构和材料参数，而无需专业知识。此外，当应用软限制时，可以优化其他物理属性，例如吸收器厚度。作为说明性的例子，我们使用神经网络来设计宽带微波吸收器，其厚度接近Kramers-Kronig关系所施加的因果关系极限。我们的方法为物理设备的反向工程提供了新的见解。
Designing microwave absorbers with customized spectrums is an attractive topic in both scientific and engineering communities. However, due to the massive number of design parameters involved, the design process is typically time-consuming and computationally expensive. To address this challenge, machine learning has emerged as a powerful tool for optimizing design parameters. In this work, we present an analytical model for an absorber composed of a multi-layered metasurface and propose a novel inverse design method based on a constrained tandem neural network. The network can provide structural and material parameters optimized for a given absorption spectrum, without requiring professional knowledge. Furthermore, additional physical attributes, such as absorber thickness, can be optimized when soft constraints are applied. As an illustrative example, we use the neural network to design broadband microwave absorbers with a thickness close to the causality limit imposed by the Kramers-Kronig relation. Our approach provides new insights into the reverse engineering of physical devices.
