Abstract A model-agnostic data enhancement (MADE) algorithm is proposed to comprehensively investigate the circular dichroism (CD) properties in the higher-order diffracted patterns of two-dimensional (2D) chiral metamaterials possessing different parameters. A remarkable feature of MADE algorithm is that it leverages substantially less data from a target problem and some training data from another already solved topic to generate a domain adaptation dataset, which is then used for model training at no expense of abundant computational resources. Specifically, nine differently shaped 2D chiral metamaterials with different unit period and one special sample containing multiple chiral parameters are both studied utilizing the MADE algorithm where three machine learning models (i.e, artificial neural network, random forest regression, support vector regression) are applied. The conventional rigorous coupled wave analysis approach is adopted to capture CD responses of these metamaterials and then assist the training of MADE, while the additional training data are obtained from our previous work. Significant evaluations regarding optical chirality in 2D metamaterials possessing various shape, unit period, width, bridge length, and separation length are performed in a fast, accurate, and data-friendly manner. The MADE framework introduced in this work is extremely important for the large-scale, efficient design of 2D diffractive metamaterials and more advanced photonic devices.

中文翻译：

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利用强大的模型不可知数据增强算法在二维衍射手性超材料中加速圆二色性预测和工程

摘要 提出了一种与模型无关的数据增强 (MADE) 算法，以全面研究具有不同参数的二维 (2D) 手性超材料的高阶衍射图案中的圆二色性 (CD) 特性。MADE 算法的一个显着特点是它利用来自目标问题的更少的数据和来自另一个已经解决的主题的一些训练数据来生成域适应数据集，然后将其用于模型训练，而无需牺牲丰富的计算资源。具体而言，九种具有不同单位周期的不同形状的二维手性超材料和一种包含多个手性参数的特殊样本都利用 MADE 算法进行了研究，其中三种机器学习模型（即人工神经网络、随机森林回归、支持向量回归）。采用传统的严格耦合波分析方法来捕获这些超材料的 CD 响应，然后辅助 MADE 的训练，而额外的训练数据来自我们之前的工作。以快速、准确和数据友好的方式对具有各种形状、单位周期、宽度、桥长度和分离长度的二维超材料中的光学手性进行了重要评估。这项工作中引入的 MADE 框架对于二维衍射超材料和更先进的光子器件的大规模、高效设计极为重要。而额外的训练数据是从我们之前的工作中获得的。以快速、准确和数据友好的方式对具有各种形状、单位周期、宽度、桥长度和分离长度的二维超材料中的光学手性进行了重要评估。这项工作中引入的 MADE 框架对于二维衍射超材料和更先进的光子器件的大规模、高效设计极为重要。而额外的训练数据是从我们之前的工作中获得的。以快速、准确和数据友好的方式对具有各种形状、单位周期、宽度、桥长度和分离长度的二维超材料中的光学手性进行了重要评估。这项工作中引入的 MADE 框架对于二维衍射超材料和更先进的光子器件的大规模、高效设计极为重要。