  We introduce conditional PINNs (physics informed neural networks) for
estimating the solution of classes of eigenvalue problems. The concept of PINNs
is expanded to learn not only the solution of one particular differential
equation but the solutions to a class of problems. We demonstrate this idea by
estimating the coercive field of permanent magnets which depends on the width
and strength of local defects. When the neural network incorporates the physics
of magnetization reversal, training can be achieved in an unsupervised way.
There is no need to generate labeled training data. The presented test cases
have been rigorously studied in the past. Thus, a detailed and easy comparison
with analytical solutions is made. We show that a single deep neural network
can learn the solution of partial differential equations for an entire class of
problems.
