  We study the full 3d static micromagnetic equations via a physics-informed
neural network (PINN) ansatz for the continuous magnetization configuration.
PINNs are inherently mesh-free and unsupervised learning models. In our
approach we can learn to minimize the total Gibbs free energy with additional
conditional parameters, such as the exchange length, by a single low-parametric
neural network model. In contrast, traditional numerical methods would require
the computation and storage of a large number of solutions to interpolate the
continuous spectrum of quasi-optimal magnetization configurations. We also
consider the important and computationally expensive stray field problem via
PINNs, where we use a basically linear learning ansatz, called extreme learning
machines (ELM) within a splitting method for the scalar potential. This reduces
the stray field training to a linear least squares problem with precomputable
solution operator. We validate the stray field method by means of numerical
example comparisons from literature and illustrate the full micromagnetic
approach via the NIST $\mu$MAG standard problem $\#3$.
