A multi-layer neural network approach for solving fractional heat equations
In this study, a new multi-layer neural network (MLNN) approach designed to solve fractional heat equations (FHEs) is introduced. To handle the fractional derivative, the Laplace transform for approximation was applied. The results of our approach with those obtained using the finite difference me...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118498/ http://psasir.upm.edu.my/id/eprint/118498/1/118498.pdf |
| _version_ | 1848867528705572864 |
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| author | Ali, Amina Senu, Norazak Ahmadian, Ali |
| author_facet | Ali, Amina Senu, Norazak Ahmadian, Ali |
| author_sort | Ali, Amina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In this study, a new multi-layer neural network (MLNN) approach designed to solve fractional heat equations (FHEs) is
introduced. To handle the fractional derivative, the Laplace transform for approximation was applied. The results of our
approach with those obtained using the finite difference method(FDM) are compared. The findings highlight the flexibility and computational efficiency of the proposed approach, making it a promising technique for solving FHEs. |
| first_indexed | 2025-11-15T14:37:56Z |
| format | Conference or Workshop Item |
| id | upm-118498 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:37:56Z |
| publishDate | 2025 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1184982025-07-15T06:49:30Z http://psasir.upm.edu.my/id/eprint/118498/ A multi-layer neural network approach for solving fractional heat equations Ali, Amina Senu, Norazak Ahmadian, Ali In this study, a new multi-layer neural network (MLNN) approach designed to solve fractional heat equations (FHEs) is introduced. To handle the fractional derivative, the Laplace transform for approximation was applied. The results of our approach with those obtained using the finite difference method(FDM) are compared. The findings highlight the flexibility and computational efficiency of the proposed approach, making it a promising technique for solving FHEs. 2025 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/118498/1/118498.pdf Ali, Amina and Senu, Norazak and Ahmadian, Ali (2025) A multi-layer neural network approach for solving fractional heat equations. In: 16th International Conference on Thermal Engineering: Theory and Applications (ICTEA), 18-20 Jun 2025, Bucharest, Romania. (pp. 1-3). https://journals.library.torontomu.ca/index.php/ictea/article/view/2664 |
| spellingShingle | Ali, Amina Senu, Norazak Ahmadian, Ali A multi-layer neural network approach for solving fractional heat equations |
| title | A multi-layer neural network approach for solving fractional heat equations |
| title_full | A multi-layer neural network approach for solving fractional heat equations |
| title_fullStr | A multi-layer neural network approach for solving fractional heat equations |
| title_full_unstemmed | A multi-layer neural network approach for solving fractional heat equations |
| title_short | A multi-layer neural network approach for solving fractional heat equations |
| title_sort | multi-layer neural network approach for solving fractional heat equations |
| url | http://psasir.upm.edu.my/id/eprint/118498/ http://psasir.upm.edu.my/id/eprint/118498/ http://psasir.upm.edu.my/id/eprint/118498/1/118498.pdf |