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Error estimation using neural network technique for solving ordinary differential equationsopen access

Authors
Nam, H.Baek, K.R.Bu, S.
Issue Date
1-Dec-2022
Publisher
Springer Science and Business Media Deutschland GmbH
Keywords
Deferred correction method; Error correction method; Neural network; Ordinary differential equations
Citation
Advances in Continuous and Discrete Models, v.2022, no.1
Journal Title
Advances in Continuous and Discrete Models
Volume
2022
Number
1
URI
https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/30112
DOI
10.1186/s13662-022-03718-4
ISSN
2731-4235
2731-4235
Abstract
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. © 2022, The Author(s).
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College of Science and Technology (Department of Software and Communications Engineering)
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