This rapid communication devises a Neural Induction Machine (NeuIM) model,
which pilots the use of physics-informed machine learning to enable AI-based
electromagnetic transient simulations. The contributions are threefold: (1) a
formation of NeuIM to represent the induction machine in phase domain; (2) a
physics-informed neural network capable of capturing fast and slow IM dynamics
even in the absence of data; and (3) a data-physics-integrated hybrid NeuIM
approach which is adaptive to various levels of data availability. Extensive
case studies validate the efficacy of NeuIM and in particular, its advantage
over purely data-driven approaches.

本快速通信在神经感应机（NeuIM）模型方面进行了研究，通过使用物理知识驱动的机器学习来实现基于人工智能的电磁暂态模拟。它的贡献有三个方面：（1）建立了 NeuIM 模型以在相位域中表示感应机；（2）提出了一种物理知识驱动的神经网络，能够在没有数据的情况下捕捉快速和慢速感应机动态；（3）提出了一种数据 - 物理集成的混合 NeuIM 方法，能够适应各种数据可用性水平。广泛的案例研究验证了 NeuIM 的有效性，特别是其相对于纯数据驱动方法的优势。