Physics-Informed Deep Learning for Time-Domain Electromagnetic Radiation Problem

We explore the application of physics-informed deep learning to solve time-domain electromagnetic problems. This method takes advantage of the differentiability of neural networks and fully integrated with first principles. Compared to traditional approach, there is no need of discretization. Numerical experiment verifies the accuracy of this scheme.