Principle and Application of Physics-Inspired Neural Networks for Electromagnetic Problems

The interpretability and generalizability of neural networks are well-aware issues in traditional deep learning due to the black-box nature of pure data-driven neural networks. While the physics-inspired neural networks (PINNs) can achieve a more generalized supervised model under few-shot learning by taking the physical principles as prior information into the network design. Especially in the electromagnetic field, the applications and realization of the PINN based on electromagnetic information are important. It can help solve the few-shot learning problems and improve the generalization of deep learning for electromagnetic data. This paper introduces several PINN models for electromagnetic problems, which can significantly reduce the reliance on the training sample size under the same level of networks parameters.