NEURAL-NETWORK-BASED APPROXIMATIONS FOR SOLVING PARTIAL-DIFFERENTIAL EQUATIONS

被引:313
|
作者
DISSANAYAKE, MWMG
PHANTHIEN, N
机构
[1] Department of Mechanical Engineering, The University of Sydney, Sydney, New South Wales
来源
关键词
D O I
10.1002/cnm.1640100303
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A numerical method, based on neural-network-based functions, for solving partial differential equations is reported in the paper. Using a 'universal approximator' based on a neural network and point collocation, the numerical problem of solving the partial differential equation is transformed to an unconstrained minimization problem. The method is extremely easy to implement and is suitable for obtaining an approximate solution in a short period of time. The technique is illustrated with the aid of two numerical examples.
引用
收藏
页码:195 / 201
页数:7
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