This research aims to apply physics-informed neural networks (PINNs) to static magnetic field problems derived from Maxwell's equations and evaluate their performance. There are few application examples of PINNs in the electromagnetic field. It is a difficult problem even with conventional numerical analysis methods such as the finite element method. This research reports the results of numerical experiments applied to two-dimensional magnetostatic field problems and performs numerical simulations with a large number of collocation points.