On the neural network solution of stiff initial value problems

Autor:	Ioannis Th. Famelis, Vaso Kaloutsa
Rok vydání:	2020
Předmět:	Mathematical problem Artificial neural network Differential equation Computer science Activation function Applied mathematics Initial value problem Point (geometry) Computational intelligence MATLAB computer computer.programming_language
Zdroj:	INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019.
ISSN:	0094-243X
DOI:	10.1063/5.0026823
Popis:	Computational Intelligence techniques are becoming more and more popular in the treatment of classical mathematical problems. The use of Neural Networks (NN) for the solution of Differential Equations is not a new perspective, as this scientific area is active for more than twenty five years. Nevertheless, there are many aspects that can be studied in both theoretical and technical point of view. In this work we investigate the NN based approximate solution of stiff Initial Value Problems (IVPs). We extend the work of Lagaris et al [1] and propose an alternative NN architecture and activation function choices. In order to reveal the capabilities of NN solutions we compare our solutions to the solutions of standard MATLAB stiff solvers.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9d0c0e4f76904e1b73b6250443bf4c96 https://doi.org/10.1063/5.0026823 Zobrazit plný text záznamu