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摘要: 自19世纪建立麦克斯韦方程以来，计算电磁学经历了百年的稳定发展，现已发展出有限差分法、有限元法、矩量法等数值算法和高频近似方法，是现代电子与信息领域的重要基石。近年来，人工智能技术经历了蓬勃发展，因其强大的建模和推理能力在电磁学界崭露头角，催生出智能电磁计算这一新兴研究方向，吸引了国内外众多科研工作者致力于该领域的研究，在电磁建模与仿真、电磁新材料和器件的分析与综合、探测与感知等领域涌现出很多优秀成果，为发展百余年的电磁学注入了新鲜血液。该文讨论了智能电磁计算的若干进展，为读者入门并了解该领域最新的研究成果提供有益帮助。Abstract: Since the introduction of Maxwell’s equations in the 19th century, computational electromagnetics has dramatically increased development. This growth can be attributed to the evolution of numerical algorithms, such as the finite difference method, finite element method, method of moments, and high-frequency approximation methods. These numerical techniques have become a crucial foundation of modern electronic and information engineering. Artificial intelligence has recently witnessed considerable development in electromagnetics; the rapid growth within this field owes itself to its robust modeling and inferential capability. This advancement has given rise to the emerging field of intelligent electromagnetic computing, which has captured the attention of numerous researchers. Remarkable achievements include electromagnetic modeling and simulation, analysis and synthesis of new electromagnetic materials and devices, and detection and perception. These contributions have injected fresh insights into the realm of electromagnetics. This paper discusses recent advances in intelligent electromagnetic computing to highlight new perspectives and avenues in research in this emerging field.
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图 1 数据驱动的正向电磁计算分类
Figure 1. The classification of data-driven forward electromagnetic computing
图 2 部分数据驱动正向电磁计算研究成果
Figure 2. Several research results of data-driven forward electromagnetic computing
图 3 部分物理驱动以及算子学习正向计算研究成果
Figure 3. Several research results of PINN based and operator-learning based forward computing
图 4 部分可微分正向电磁计算研究成果示意
Figure 4. Several research results of differentiable forward electromagnetic computing
图 5 基于U-Net结构的逆向智能电磁成像
Figure 5. Reverse intelligent electromagnetic imaging based on U-Net
图 6 基于Pix2Pix结构的逆向智能电磁成像
Figure 6. Reverse intelligent electromagnetic imaging based on Pix2Pix
图 7 借鉴迭代优化算法的端到端逆向智能电磁成像
Figure 7. End-to-end reverse intelligent electromagnetic imaging inspired by iterative optimization methods
10 基于信息超材料的智能方向图设计
10. Intelligent design of radiation patterns based on information metamaterial
图 11 基于信息超材料的非监督智能设计
Figure 11. Unsupervised intelligent design based on information metamaterial
图 12 用于复杂系统的信息超材料的智能化设计
Figure 12. Intelligent design of information metamaterial for complex systems
表 1 4种智能电磁计算方法特性对比
Table 1. Comparison of characteristics of four intelligent electromagnetic calculation methods
类别 应用成熟度 计算复杂度 泛化能力 训练数据量 数据驱动(结果学习) 高 低 差 多 数据驱动(过程学习) 较高 适中 较好 适中 物理驱动 中等 较低 适中 少 算子学习 低 低 较好 较多 可微分计算 中等 高 好 少 
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