2023/09/29 by Qing Shen, Yifan Zhou, Shen, Qing +3
Engineering · Materials Science · Physics and Astronomy · #Electric Motor Design and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Magnetic Properties and Applications #Model Reduction and Neural Networks #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.16943
openalex publication_date 2023/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.