## 3 Construction and Application of Magnetic Field Visualization Teaching Platform
### Magnetic Field Visualization Teaching Platform  
The engineering electromagnetic field visualization teaching software includes two parts: input parameters and calculation drawing. The three parameters of coil current, coil turns, and magnetization model can be changed in the \"Input Parameters\" menu. The cloud image is calculated after the parameter setting is completed and the \"Calculate\" button is clicked. After the calculation is completed, the cloud diagram of the calculation result is displayed in the drawing area. Click the \"Magnetic field lines\" button to display the magnetic field lines. Students can freely set various parameters, obtain the magnetic flux density distribution cloud map of the magnetic core inductor under different parameters through software calculation, and analyze the calculation results. When the core material is linear, students can manually change the relative permeability. When the core material is nonlinear, students can use the \(\mathbf{B}\)-\(\mathbf{H}\) curve to calculate. When the relative permeability is selected as the magnetization model, an application example is shown in Figure 12. The version number of MATLAB used is R2016a, from North China Electric Power University, Baoding, China. The calculation results obtained by different calculation methods at the same position in the magnetic field were compared statistically. The magnetic flux density at the center point of the inductance was recorded and compared with the finite element calculation value. The results are shown in Figure 13. It can be seen that the neural network (NN) value was very close to the finite element method (FEM) value for different coil currents. The maximum relative error was about 2.69%, which meets the needs of electromagnetic field teaching and engineering applications. Figure 9: The neural network prediction result. Figure 10: Comparison of cloud images of ui-shaped inductor: (**a**) the finite element calculation results and (**b**) the neural network prediction result. When the magnetization model is the \(\mathbf{B}\)-\(\mathbf{H}\) curve, the application example is shown in Figure 14.  
Similar to Figure 13, the magnetic flux density at the center of the inductor was recorded. The results of comparing the calculated values of the neural network with those calculated by the finite element are shown in Figure 15. It can be seen that under different working conditions, the maximum relative error of the two calculation methods was about 1.89%. The calculation results meet the needs of electromagnetic field teaching and engineering applications. Figure 11: Comparison of magnetic flux density values of ui-shaped inductor. Figure 12: Neural network prediction results when the magnetization model is relative permeability. The interactive interface of the MATLAB-based magnetic field visualization teaching software is friendly and clear, and the operation steps are simple and easy to learn. In terms of teaching arrangements, teachers can not only demonstrate and lead students to learn about engineering electromagnetic fields in class, but also assign the operation of the visualization software as homework after explaining the relevant theoretical basis, leaving it to the students to explore and complete by themselves. These teaching arrangements can fully mobilize students' subjective initiative, cultivate students' autonomous learning ability, and also enable students to consolidate what they have learned after class and deepen their understanding of what they have learned [25; 26; 27]. Compared with the teaching mode adopted in traditional engineering electromagnetic field classroom teaching, the use of engineering electromagnetic field visualization teaching software to assist teaching has the following advantages:  
1. The visualization effect is good. The graphical interface of the engineering electromagnetic field visualization teaching software is easy to control.