Approximating Excited States using Neural Networks
Computational Physics
2021-10-04 v5 Disordered Systems and Neural Networks
Abstract
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.
Cite
@article{arxiv.2012.13268,
title = {Approximating Excited States using Neural Networks},
author = {Yimeng Min},
journal= {arXiv preprint arXiv:2012.13268},
year = {2021}
}