Time-Domain 3D Electromagnetic Fields Estimation Based on Physics-Informed Deep Learning Framework

Electromagnetic simulation is important and time-consuming in RF/microwave circuit design. Physics-informed deep learning is a promising method to learn a family of parametric partial differential equations. In this work, we propose a physics-informed deep learning framework to estimate time-domain 3D electromagnetic fields. Our method leverages physics-informed loss functions to model Maxwell's equations which govern electromagnetic fields. Our post-trained model produces accurate results with over 200 × speedup over the FDTD simulation. We reduce the mean square error by at least 14% and 15%, with respect to purely data-driven learning and the Fourier operator learning method FNO. In order to optimize data and physical loss simultaneously, we introduce a self-adaptive scaling factors updating algorithm, which has 8.4% less error than the loss balancing method ReLoBRaLo. Our codes are open-source at this link.