  Physics-informed neural networks (PINNs) have emerged as a promising
numerical method based on deep learning for modeling boundary value problems,
showcasing promising results in various fields. In this work, we use PINNs to
discretize three-dimensional electromagnetic, parametric problems, with
material discontinuities, covering both static and transient regimes. By
replacing the discontinuous material properties with a continuous
approximation, we eliminate the need to directly enforce interface conditions.
Using the Neural Tangent Kernel (NTK) analysis, we show that using the
first-order formulation of Maxwell's equations is more suitable for interface
problems. We introduce a PINN-based decomposition on overlapping domains to
enhance the convergence rate of the PINN.
