  In this work, we explore the ability of NN (Neural Networks) to serve as a
tool for finding eigen-pairs of ordinary differential equations. The question
we aime to address is whether, given a self-adjoint operator, we can learn what
are the eigenfunctions, and their matching eigenvalues. The topic of solving
the eigen-problem is widely discussed in Image Processing, as many image
processing algorithms can be thought of as such operators. We suggest an
alternative to numeric methods of finding eigenpairs, which may potentially be
more robust and have the ability to solve more complex problems. In this work,
we focus on simple problems for which the analytical solution is known. This
way, we are able to make initial steps in discovering the capabilities and
shortcomings of DNN (Deep Neural Networks) in the given setting.
