  In this study, we introduce a novel approach for deriving the solution of the
ideal force-free steady-state pulsar magnetosphere in three dimensions. Our
method involves partitioning the magnetosphere into the regions of closed and
open field lines, and subsequently training two custom Physics Informed Neural
Networks (PINNs) to generate the solution within each region. We periodically
modify the shape of the boundary separating the two regions (the separatrix) to
ensure pressure balance throughout. Our approach provides an effective way to
handle mathematical contact discontinuities in Force-Free Electrodynamics
(FFE). We present preliminary results in axisymmetry, which underscore the
significant potential of our method. Finally, we discuss the challenges and
limitations encountered while working with Neural Networks, thus providing
valuable insights from our experience.
