论文标题
使用张量神经网络求解Schrödinger方程
Solving Schrödinger Equation Using Tensor Neural Network
论文作者
论文摘要
在本文中，我们介绍了一种新颖的方法，可以通过张量神经网络解决多体Schrodinger方程。基于张量的产品结构，我们可以通过使用可耐受的计算复杂性中张量神经网络构建的函数来完成直接数值集成。特别是，我们设计了几种有效的数值方法，以高精度处理可变耦合的库仑电位。相应的机器学习方法旨在求解多体Schrodinger方程。提供了一些数值示例，以验证所提出算法的准确性和效率。
In this paper, we introduce a novel approach to solve the many-body Schrodinger equation by the tensor neural network. Based on the tensor product structure, we can do the direct numerical integration by using fixed quadrature points for the functions constructed by the tensor neural network within tolerable computational complexity. Especially, we design several types of efficient numerical methods to treat the variable-coupled Coulomb potentials with high accuracy. The corresponding machine learning method is built for solving many-body Schrodinger equation. Some numerical examples are provided to validate the accuracy and efficiency of the proposed algorithms.
