Abstract The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences.

中文翻译：

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光子晶体的预测和生成机器学习模型

摘要 光子特征的预测和设计传统上由理论驱动的计算方法指导，涵盖范围广泛的直接求解器和优化技术。受机器学习领域巨大进步的推动，最近人们对开发用于光子学的互补数据驱动方法越来越感兴趣。在这里，我们展示了几种用于光子晶体表征和逆向设计的预测性和生成性数据驱动方法。具体而言，我们构建了一个包含 20,000 个二维光子晶体晶胞及其相关能带结构的数据集，从而能够训练监督学习模型。使用这些数据集，我们展示了一个用于能带结构预测的高精度卷积神经网络，与传统的理论驱动求解器相比，具有数量级的加速。另外，我们展示了一种通过生成对抗网络对光子晶体进行高通量逆向设计的方法，其设计目标是大量的横向磁带隙。我们的工作强调光子晶体作为一个自然应用领域和测试平台，用于开发光子学和自然科学中的数据驱动工具。