  Eigenvalue problems are critical to several fields of science and
engineering. We present a novel unsupervised neural network for discovering
eigenfunctions and eigenvalues for differential eigenvalue problems with
solutions that identically satisfy the boundary conditions. A scanning
mechanism is embedded allowing the method to find an arbitrary number of
solutions. The network optimization is data-free and depends solely on the
predictions. The unsupervised method is used to solve the quantum infinite well
and quantum oscillator eigenvalue problems.

Computational Physics;Machine Learning