Hou Zheyu, Tang Tingting, Shen Jian, Li Chaoyang, Li Fuyu
Hainan University, No. 58, Renmin Avenue, Haikou, 570228, Hainan Province, China.
Chengdu University of Information Technology, Chengdu, 610225, China.
Nanoscale Res Lett. 2020 Apr 15;15(1):83. doi: 10.1186/s11671-020-03319-8.
The introduction of "metamaterials" has had a profound impact on several fields, including electromagnetics. Designing a metamaterial's structure on demand, however, is still an extremely time-consuming process. As an efficient machine learning method, deep learning has been widely used for data classification and regression in recent years and in fact shown good generalization performance. We have built a deep neural network for on-demand design. With the required reflectance as input, the parameters of the structure are automatically calculated and then output to achieve the purpose of designing on demand. Our network has achieved low mean square errors (MSE), with MSE of 0.005 on both the training and test sets. The results indicate that using deep learning to train the data, the trained model can more accurately guide the design of the structure, thereby speeding up the design process. Compared with the traditional design process, using deep learning to guide the design of metamaterials can achieve faster, more accurate, and more convenient purposes.
“超材料”的引入对包括电磁学在内的多个领域产生了深远影响。然而，按需设计超材料的结构仍然是一个极其耗时的过程。作为一种高效的机器学习方法，深度学习近年来已广泛用于数据分类和回归，并且实际上表现出良好的泛化性能。我们构建了一个用于按需设计的深度神经网络。以所需的反射率作为输入，自动计算结构的参数，然后输出以实现按需设计的目的。我们的网络实现了低均方误差（MSE），训练集和测试集的MSE均为0.005。结果表明，使用深度学习训练数据，训练后的模型可以更准确地指导结构设计，从而加快设计过程。与传统设计过程相比，使用深度学习指导超材料设计可以实现更快、更准确和更便捷的目的。
