论文标题
使用神经网络近似激发态
Approximating Excited States using Neural Networks
论文作者
论文摘要
最近开发的基于神经网络的波浪功能方法能够实现最新的结果，以在实际空间中找到基态。在这项工作中，基于神经网络的方法用于计算激发态。我们通过差异原理训练我们的网络，沿着进一步的惩罚术语施加了较低能量的本征函数。为了证明这种方法的有效性，提出了一维和二维谐波振荡器的数值计算。
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.
