A standardized hybrid deep-learning model based on a combination of a deep convolutional network and a recurrent neural network is proposed to predict the optical response of metasurfaces considering their shape and all the important dimensional parameters (such as periodicity, height, width, and aspect ratio) simultaneously. It is further used to aid the design procedure of the key components of solar thermophotovoltaics (STPVs), i.e., metasurface based perfect solar absorbers and selective emitters. Although these planar meta-absorbers and meta-emitters offer an ideal platform to realize compact and efficient STPV systems, a conventional procedure to design these is time taking, laborious, and computationally exhaustive. The optimization of such planar devices needs hundreds of EM simulations, where each simulation requires multiple iterations to solve Maxwell's equations on a case-by-case basis. To overcome these challenges, we propose a unique deep learning-based model that generates the most likely optical response by taking images of the unit cells as input. The proposed model uses a deep residual convolutional network to extract the features from the images followed by a gated recurrent unit to infer the desired optical response. Two datasets having considerable variance are collected to train the proposed network by simulating randomly shaped nanostructures in CST microwave studio with periodic boundary conditions over the desired wavelength ranges. These simulations yield the optical absorption/emission response as the target labels. The proposed hybrid configuration and transfer learning provide a generalized model to infer the absorption/emission spectrum of solar absorbers/emitters within a fraction of seconds with high accuracy, regardless of its shape and dimensions. This accuracy is defined by the regression metric mean square error (MSE), where the minimum MSE achieved for absorbers and emitters test datasets are 7.3 × 10⁻⁰⁴ and 6.2 × 10⁻⁰⁴ respectively. The trained model can also be fine-tuned to predict the absorption response of different thin film refractory materials. To enhance the diversity of the model. Thus it aids metasurface design procedure by replacing the conventional time-consuming and computationally exhaustive numerical simulations and electromagnetic (EM) software. The comparison of the average simulation time (for 10 samples) and the average DL model prediction time shows that the DL model works about 98% faster than the conventional simulations. We believe that the proposed methodology will open new research directions towards more challenging optimization problems in the field of electromagnetic metasurfaces.

中文翻译：

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基于深度学习的混合序列建模，用于 STPV 应用的超表面光学响应检索

提出了一种基于深度卷积网络和循环神经网络组合的标准化混合深度学习模型，以预测超表面的光学响应，同时考虑其形状和所有重要的维度参数（如周期性、高度、宽度和纵横比）比）同时。它进一步用于辅助太阳能热光伏 (STPV) 关键组件的设计过程，即基于超表面的完美太阳能吸收器和选择性发射器。尽管这些平面元吸收器和元发射器为实现紧凑高效的 STPV 系统提供了理想的平台，但设计这些系统的传统程序耗时、费力且计算量大。这种平面器件的优化需要数百次 EM 模拟，其中每个模拟都需要多次迭代才能逐案求解麦克斯韦方程组。为了克服这些挑战，我们提出了一种独特的基于深度学习的模型，该模型通过将晶胞的图像作为输入来生成最可能的光学响应。所提出的模型使用深度残差卷积网络从图像中提取特征，然后是门控循环单元来推断所需的光学响应。通过在所需波长范围内具有周期性边界条件的 CST 微波工作室中模拟随机形状的纳米结构，收集了具有相当大差异的两个数据集来训练所提出的网络。这些模拟产生光学吸收/发射响应作为目标标记。所提出的混合配置和转移学习提供了一个通用模型，可以在几分之一秒内以高精度推断太阳能吸收器/发射器的吸收/发射光谱，而不管其形状和尺寸如何。该精度由回归度量均方误差 (MSE) 定义，其中吸收器和发射器测试数据集实现的最小 MSE 为 7.3 × 10^-04和 6.2 × 10 ^-04分别。训练后的模型还可以进行微调，以预测不同薄膜耐火材料的吸收响应。增强模型的多样性。因此，它通过取代传统的耗时且计算量大的数值模拟和电磁 (EM) 软件来帮助超表面设计过程。平均模拟时间（10 个样本）与平均 DL 模型预测时间的比较表明，DL 模型的工作速度比传统模拟快 98%。我们相信所提出的方法将为电磁超表面领域更具挑战性的优化问题开辟新的研究方向。