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Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation

Physics-informed neural networks (PINN) are an artificial neural network (ANN) approach for solving differential equations. PINN offers an alternative to classical numerical methods. The paper discusses the applications of PINN in various domains by highlighting the advantages, challenges, limitatio...

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Main Authors: Abdullah, null, Faye, Ibrahima, Laila Amera, Aziz
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2023
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/42899/1/Artificial%20neural%20networks%20solutions%20for%20solving%20differential%20equations.pdf
http://umpir.ump.edu.my/id/eprint/42899/
https://doi.org/10.37934/arfmts.112.1.7683
https://doi.org/10.37934/arfmts.112.1.7683
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spelling my.ump.umpir.428992025-01-08T02:03:50Z http://umpir.ump.edu.my/id/eprint/42899/ Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation Abdullah, null Faye, Ibrahima Laila Amera, Aziz Q Science (General) QA Mathematics Physics-informed neural networks (PINN) are an artificial neural network (ANN) approach for solving differential equations. PINN offers an alternative to classical numerical methods. The paper discusses the applications of PINN in various domains by highlighting the advantages, challenges, limitations, and some future directions. For example, PINN is implemented to solve the differential equations describing the Flow of Viscoelastic Fluid with Microrotation at a Horizontal Circular Cylinder Boundary Layer. The differential equations resulting from a nondimensionalization process of the governing equations and the associated boundary conditions are solved using PINN. The obtained results using PINN are discussed and compared to other state-of-the-art methods. Future research might aim to increase the precision and effectiveness of PINN models for solving differential equations, either by adding more physics-based restrictions or multi-scale methods to expand their capabilities. Additionally, investigating new application domains like linked multi-physics issues or real-time simulation situations may help to increase the reach and significance of PINN approaches. Semarak Ilmu Publishing 2023-12 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42899/1/Artificial%20neural%20networks%20solutions%20for%20solving%20differential%20equations.pdf Abdullah, null and Faye, Ibrahima and Laila Amera, Aziz (2023) Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 112 (1). pp. 76-83. ISSN 2289-7879. (Published) https://doi.org/10.37934/arfmts.112.1.7683 https://doi.org/10.37934/arfmts.112.1.7683
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Abdullah, null
Faye, Ibrahima
Laila Amera, Aziz
Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation
description Physics-informed neural networks (PINN) are an artificial neural network (ANN) approach for solving differential equations. PINN offers an alternative to classical numerical methods. The paper discusses the applications of PINN in various domains by highlighting the advantages, challenges, limitations, and some future directions. For example, PINN is implemented to solve the differential equations describing the Flow of Viscoelastic Fluid with Microrotation at a Horizontal Circular Cylinder Boundary Layer. The differential equations resulting from a nondimensionalization process of the governing equations and the associated boundary conditions are solved using PINN. The obtained results using PINN are discussed and compared to other state-of-the-art methods. Future research might aim to increase the precision and effectiveness of PINN models for solving differential equations, either by adding more physics-based restrictions or multi-scale methods to expand their capabilities. Additionally, investigating new application domains like linked multi-physics issues or real-time simulation situations may help to increase the reach and significance of PINN approaches.
format Article
author Abdullah, null
Faye, Ibrahima
Laila Amera, Aziz
author_facet Abdullah, null
Faye, Ibrahima
Laila Amera, Aziz
author_sort Abdullah, null
title Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation
title_short Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation
title_full Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation
title_fullStr Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation
title_full_unstemmed Artificial neural networks solutions for solving differential equations: A focus and example for flow of viscoelastic fluid with microrotation
title_sort artificial neural networks solutions for solving differential equations: a focus and example for flow of viscoelastic fluid with microrotation
publisher Semarak Ilmu Publishing
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/42899/1/Artificial%20neural%20networks%20solutions%20for%20solving%20differential%20equations.pdf
http://umpir.ump.edu.my/id/eprint/42899/
https://doi.org/10.37934/arfmts.112.1.7683
https://doi.org/10.37934/arfmts.112.1.7683
_version_ 1822924894307876864
score 13.23648

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