A comparative artificial neural networks for Schwarzschild black hole (SBH) radius
It is consensus among researchers that the data for the black holes is complicated and extremely non-linear in nature. Therefore, it remains a challenging task for them to predict the key characteristics of concerned black holes accurately. The present work offers artificial neural networks assistan...
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Elsevier
2025-08-01
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| Series: | Physics Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666032625000377 |
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| author | Khalil Ur Rehman Wasfi Shatanawi Weam G. Alharbi |
| author_facet | Khalil Ur Rehman Wasfi Shatanawi Weam G. Alharbi |
| author_sort | Khalil Ur Rehman |
| collection | DOAJ |
| description | It is consensus among researchers that the data for the black holes is complicated and extremely non-linear in nature. Therefore, it remains a challenging task for them to predict the key characteristics of concerned black holes accurately. The present work offers artificial neural networks assistance in the context of a choice of training functions for the prediction of astrophysical phenomena like the event horizon and radius of black holes. To be more specific, we considered the Schwarzschild black hole as the simplest solution of Einstein's field equations. The Schwarzschild radius and masses are chosen in the last and first layers of the neural networks model, respectively. Two various training functions namely Levenberg-Marquardt training algorithm (LMTA) and Scaled Conjugate Gradient training algorithms (SCGTA) are used. We have observed that the LMTA achieved significantly lower error rates, suggesting a better fit and stronger learning capabilities from the solar masses of black holes. Furthermore, the close alignment between the ANN-predicted and actual Schwarzschild black hole radius demonstrates the LMTA model holds the ability to generalize effectively across unseen masses of black holes. |
| format | Article |
| id | doaj-art-50ccc268a20a48c19a7806cbbf21b249 |
| institution | OA Journals |
| issn | 2666-0326 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics Open |
| spelling | doaj-art-50ccc268a20a48c19a7806cbbf21b2492025-08-20T02:09:52ZengElsevierPhysics Open2666-03262025-08-012410028710.1016/j.physo.2025.100287A comparative artificial neural networks for Schwarzschild black hole (SBH) radiusKhalil Ur Rehman0Wasfi Shatanawi1Weam G. Alharbi2Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia; Corresponding author.Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586, Saudi ArabiaDepartment of Mathematics, Faculty of Science, University of Tabuk, Tabuk, 71491, Saudi ArabiaIt is consensus among researchers that the data for the black holes is complicated and extremely non-linear in nature. Therefore, it remains a challenging task for them to predict the key characteristics of concerned black holes accurately. The present work offers artificial neural networks assistance in the context of a choice of training functions for the prediction of astrophysical phenomena like the event horizon and radius of black holes. To be more specific, we considered the Schwarzschild black hole as the simplest solution of Einstein's field equations. The Schwarzschild radius and masses are chosen in the last and first layers of the neural networks model, respectively. Two various training functions namely Levenberg-Marquardt training algorithm (LMTA) and Scaled Conjugate Gradient training algorithms (SCGTA) are used. We have observed that the LMTA achieved significantly lower error rates, suggesting a better fit and stronger learning capabilities from the solar masses of black holes. Furthermore, the close alignment between the ANN-predicted and actual Schwarzschild black hole radius demonstrates the LMTA model holds the ability to generalize effectively across unseen masses of black holes.http://www.sciencedirect.com/science/article/pii/S2666032625000377Artificial intelligenceSchwarzschild black holeEvent horizonLMTASCGTANumerical data |
| spellingShingle | Khalil Ur Rehman Wasfi Shatanawi Weam G. Alharbi A comparative artificial neural networks for Schwarzschild black hole (SBH) radius Physics Open Artificial intelligence Schwarzschild black hole Event horizon LMTA SCGTA Numerical data |
| title | A comparative artificial neural networks for Schwarzschild black hole (SBH) radius |
| title_full | A comparative artificial neural networks for Schwarzschild black hole (SBH) radius |
| title_fullStr | A comparative artificial neural networks for Schwarzschild black hole (SBH) radius |
| title_full_unstemmed | A comparative artificial neural networks for Schwarzschild black hole (SBH) radius |
| title_short | A comparative artificial neural networks for Schwarzschild black hole (SBH) radius |
| title_sort | comparative artificial neural networks for schwarzschild black hole sbh radius |
| topic | Artificial intelligence Schwarzschild black hole Event horizon LMTA SCGTA Numerical data |
| url | http://www.sciencedirect.com/science/article/pii/S2666032625000377 |
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