  Physics-Informed Neural Networks (PINNs) are a new family of numerical
methods, based on deep learning, for modeling boundary value problems. They
offer an advantage over traditional numerical methods for high-dimensional,
parametric, and data-driven problems. However, they perform poorly on problems
where the solution exhibits high frequencies, such as discontinuities or sharp
gradients. In this work, we develop a PINN-based solver for modeling
three-dimensional, transient and static, parametric electromagnetic problems in
discontinuous media. We use the first-order Maxwell's equations to train the
neural network. We use a level-set function to represent the interface with a
continuous function, and to enrich the network's inputs with high-frequencies
and interface information. Finally, we validate the proposed methodology on
multiple 3D, parametric, static, and transient problems.
