A DEEP NEURAL NETWORK-BASED NUMERICAL METHOD FOR SOLVING CONTACT PROBLEMS

被引：0
|
作者：
Shen, X. I. N. G. [1 ]
Cheng, X. I. A. O. L. I. A. N. G. [1 ]
Liang, K. E. W. E., I [1 ]
Wang, X. I. L. U. [1 ]
Wu, Z. H. E. N. G. H. U. A. [1 ]
机构：
[1] Zhejiang Univ, Sch Math Sci, Hangzhou, Zhejiang, Peoples R China
来源：
JOURNAL OF NONLINEAR AND VARIATIONAL ANALYSIS | 2022年 / 6卷 / 05期
关键词：
Deep neural networks; Frictionless unilateral contact problem; Hemivariational inequality; HEMIVARIATIONAL INEQUALITIES; ALGORITHM;
D O I：
10.23952/jnva.6.2022.5.04
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we propose a deep neural network-based numerical method for solving contact problems. Focusing on a static frictionless unilateral contact problem, we derive its weak formulation and prove that the solution of the weak formulation is also the minimizer of the corresponding energy functional. By converting the original contact problem into a minimization problem, a deep neural network is adopted to approximate the solution and solve the minimization problem. Numerical results demonstrate the effectiveness and accuracy of our method.
引用
收藏
页码：483 / 498
页数：16
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