A Deep Learning Framework for Solving Rectangular Waveguide Problems

We employ a class of deep learning, known as Physics Informed Neural Networks (PINNs), to solve rectangular waveguide problems. In particular, we successfully apply PINNs to the task of solving electric and magnetic fields, which can be described by partial differential equations (PDEs). PINNs are neural networks that can naturally encode PDEs into the loss function, while partial derivatives with respect to input variables can be calculated through automatic differentiation (AD) built in modern deep learning libraries. We also show the applicability of the framework for predicting the unknown parameters such as wavenumber. We validate our model in terms of the convergence speed and accuracy. In addition, we suggest an approach to scale the input vector in order to accelerate training.