Error estimation using neural network technique for solving ordinary differential equations

被引：1
|
作者：
Nam, Haewon [1 ]
Baek, Kyung Ryeol [2 ]
Bu, Sunyoung [1 ]
机构：
[1] Hongik Univ, Dept Liberal Arts, Sejong, South Korea
[2] Hongik Univ, Ind Acad Cooperat Grp, Sejong, South Korea
来源：
ADVANCES IN CONTINUOUS AND DISCRETE MODELS | 2022年 / 2022卷 / 01期
基金：
新加坡国家研究基金会;
关键词：
Ordinary differential equations; Neural network; Deferred correction method; Error correction method; DEFERRED CORRECTION METHODS;
D O I：
10.1186/s13662-022-03718-4
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we present a numerical method to solve ordinary differential equations (ODEs) by using neural network techniques in a deferred correction method framework. Similar to the deferred or error correction techniques, a provisional solution of the ODE is preferentially calculated by any lower-order scheme to satisfy given initial conditions, and the corresponding error is investigated by fully connected neural networks and structured to obtain sufficient magnitude of the error. Numerical examples are illustrated to demonstrate the efficiency of the proposed scheme.
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收藏
页数：13
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