Learning nonlinear integral operators via recurrent neural networks and its application in solving integro-differential equations

Learning nonlinear integral operators via recurrent neural networks and its application in solving integro-differential equations

In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-differential equations (IDEs). The LSTM-RNN representation of the nonlinear integral operator allows us to turn a system o...

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Bibliographic Details
Main Authors: Hardeep Bassi, Yuanran Zhu, Senwei Liang, Jia Yin, Cian C. Reeves, Vojtěch Vlček, Chao Yang
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:Machine Learning with Applications
Subjects:
Machine learning
Integro-differential equations
Online Access:http://www.sciencedirect.com/science/article/pii/S2666827023000774

Internet

http://www.sciencedirect.com/science/article/pii/S2666827023000774

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