Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning

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Abstract
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.
Funding Information
  • Sichuan Science and Technology Program (2019JDJQ0003)
  • Open Project Program of State Key Laboratory of Marine Resource Utilization in South China Sea (2019010)
  • Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents (1)

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