  Recently developed neural network-based wave function methods are capable of
achieving state-of-the-art results for finding the ground state in real space.
In this work, a neural network-based method is used to compute excited states.
We train our network via variational principle, along a further penalty term
that imposes the orthogonality with lower-energy eigenfunctions. As a
demonstration of the effectiveness of this approach, results from numerical
calculations for one-dimensional and two-dimensional harmonic oscillators are
presented.
