A comparison of deep learning-based techniques for solving partial differential equations

Rabiu Bashir Yunus and Nooraini Zainuddin and Afza Shafie and Muhammad Izzatullah Mohd Mustafa and Samsul Ariffin Abdul Karim (2024) A comparison of deep learning-based techniques for solving partial differential equations. AIP Conference Proceedings. pp. 1-12. ISSN 0094243X

	Text ABSTRACT.pdf Download (40kB)
	Text FULL TEXT.pdf Restricted to Registered users only Download (586kB) \| Request a copy

URL: https://www.researchgate.net/publication/377203432...

Abstract

Obtaining the solutions of high-dimensional partial differential equations (PDEs) seems to be difficult by utilizing the classical numerical methods. Recently, deep neural networks (DNNs) techniques have received special attentions in solving high–dimensional problems in PDEs. In this study, our quest is to investigate some newly introduced data-driven deep learning-based approaches and compare their performance in terms of their efficiency and faster training towards highdimensional PDEs. However, the comparison is carried out based on different activation functions, number of layers and gradient based optimizers. We consider some benchmark problems in our numerical experiments which includes Burgers equation, Diffusion-reaction equation and Allen-Cahn Equations.

Item Type:	Article
Keyword:	Deep learning; PDEs; Neural Network; Data-driven
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics Q Science > QC Physics > QC1-999 Physics > QC1-75 General
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	12 Jun 2024 09:32
Last Modified:	12 Jun 2024 09:32
URI:	https://eprints.ums.edu.my/id/eprint/38814

Actions (login required)

View Item