Numerical computation of low-frequency electromagnetic fields is a core foundational technology for the electromagnetic characteristic analysis of electrical equipment. Faced with the prevalent multi-scale structural features

strong material nonlinearity

and the need for iterative solutions under various operating conditions in electrical equipment

traditional numerical algorithms face challenges in improving computational efficiency

solution accuracy

and cross-scenario generalization ability. With the development of deep learning

physics-driven neural networks

by integrating physical constraints such as control equations and initial/boundary conditions

can solve physical fields with few or no data

and become an effective method to improve the computational efficiency

to realize the model generalization

and to ensure the physical reliability of results. This paper systematically reviews the research progress of physics-driven neural networks in the rapid computation of low-frequency electromagnetic fields. First

this paper elucidates the general solution framework and basic process of this type of algorithm. Then

this paper classifies and compares mainstream algorithms according to the mathematical form of the loss function used to constrain the control equations

and focuses on reviewing application cases of various algorithms in typical electromagnetic field problems. Based on this

this paper explores the extended applications of this type of algorithm in three-dimensional electromagnetic models

and provides an in-depth analysis and discussion of its computational efficiency

engineering applicability

and related computational framework. Finally

the future development trend of this field is envisioned. It is pointed out that

through systematic advancements in following four directions:complex 3D modeling

nonlinear material characterization

deep migration mechanisms

and data-physics fusion-driven architecture

it is hoped that a novel computational method with high efficiency

high accuracy

and strong generalization ability can be provided for low-frequency electromagnetic field analysis.