• 论文 •

### 卷积神经网络求解有限元单元刚度矩阵

1. 1. 北京航空航天大学 宇航学院, 北京 100083;
2. 北京空间飞行器总体设计部, 北京 100094
• 收稿日期:2019-04-01 发布日期:2020-03-28
• 通讯作者: 贾光辉 E-mail:jiaguanghui@buaa.edu.cn
• 作者简介:贾光辉,男,博士,副教授,硕士生导师。主要研究方向:飞行器结构分析与撞击动力学响应;于云瑞,男,硕士研究生。主要研究方向:深度学习与传统结构分析的结合;王丹,女,博士,高级工程师。主要研究方向:飞行器总体设计。

### Solving finite element stiffness matrix based on convolutional neural network

JIA Guanghui1, YU Yunrui1, WANG Dan2

1. 1. School of Astronautics, Beihang University, Beijing 100083, China;
2. Beijing Institute of Spacecraft System Engineering, Beijing 100094, China
• Received:2019-04-01 Published:2020-03-28

Abstract: With the successful application and rapid development of deep learning in many fields, the integration of deep learning with traditional structural analysis has become a new research direction. In terms of solving the finite element stiffness matrix problem, the application of convolutional neural network in structural analysis is studied. Taking the quadrilateral plane stress element as an example, based on the convolutional neural network, a neural network model for solving the finite element global stiffness matrix is proposed. Moreover, the relationship between the learning effect of the network and the number of network convolution kernels and the number of training samples is analyzed. The calculation example shows that, within a certain range, the learning ability of the network increases with the number of convolution kernels and the number of training samples. In practical applications, the corresponding convolutional neural network can be set according to specific accuracy requirements. After the convolutional network training is completed, the calculation of the element stiffness matrix is real-time, and the accuracy meets the engineering requirements.