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| Citation: | JIA Yuxiang, WANG Jiafu, CHEN Wei, et al. Research progress on rapid optimization design methods of metamaterials based on intelligent algorithms[J]. Journal of Radars, 2021, 10(2): 220–239. doi: 10.12000/JR21027
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Research Progress on Rapid Optimization Design Methods of Metamaterials Based on Intelligent Algorithms
doi: 10.12000/JR21027
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Abstract
At present, research on metamaterials is continuously advancing to engineering applications, and great progress is being achieved in the areas of physical mechanisms and effects, design theory and methods, and fabrication and measurement. However, traditional metamaterials design mainly relies on artificial design and optimization. In the face of large-scale engineering applications, it is impossible to realize the rapid overall design of a large number of metamaterial structural units. In recent years, the proportion of intelligent algorithms covering traditional heuristic algorithms and neural network algorithms in metamaterials design has increased gradually. Metamaterials design based on intelligent algorithms can surpass the limitation of traditional methods in different substrate systems, frequency variation, and different performance indicators, offering the unique advantages of rapid design and architectural innovation. This paper summarizes the application of several typical intelligent algorithms, including the genetic algorithm, Hopfield network algorithm, and deep learning algorithm in metamaterials design, which include forward designs and an inverse design. The use of intelligent algorithms can achieve the rapid design of frequency selective surfaces under different performance indexes, multi-mechanisms composite absorber metamaterials, flat focusing, and abnormal reflection metasurfaces, providing the necessary support for design methods while promoting the engineering applications of metamaterials. -
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References
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- Figure 1. Application methods of neural network
- Figure 2. Topological FSS[23]
- Figure 3. The optimized bandpass FSS[23]
- Figure 4. Schematic illustration of wideband absorber topology configuration[23]
- Figure 5. Variation of absorptivity with polarization mode and incident angle[23]
- Figure 6. Metaurface absorber based on topology optimization design
- Figure 7. Structure illustration of the symmetry broken metasurface absorber[29]
- Figure 8. Comparisons between metal plate and metasurfaces[29]
- Figure 9. Low RCS coded metasurface[30]
- Figure 10. RCS far-fields[30]
- Figure 11. Topology unit[23]
- Figure 12. Coded metasurface[23]
- Figure 13. Metasurface sample[23]
- Figure 14. Optimization flow illustration based on genetic algorithm[37]
- Figure 15. Curve and metal line shape[37]
- Figure 16. Electromagnetic response of the absorber[37]
- Figure 17. Hopfield network[41]
- Figure 18. Associative memory function of Hopfield network[41]
- Figure 19. Abnormal reflection metasurface[41]
- Figure 20. Flow chart of composite optimization based on deep learning and genetic algorithm[48]
- Figure 21. Network structure of Inception V3[46]
- Figure 22. Electromagnetic performances of metasureface[46]