Artificial Neural Network Modeling of the Conformable Fractional Isothermal Gas Spheres
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| Format: | Artículo científico |
| Language: | en |
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Universidad Nacional Autónoma de México
2021
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| author | Yosry. A. Azzam |
| author_facet | Yosry. A. Azzam |
| contents | Artificial Neural Network Modeling of the Conformable Fractional Isothermal Gas Spheres Yosry. A. Azzam Emad A.-B. Abdel-Salam Mohamed I. Nouh Física, Astronomía y Matemáticas stars: neutron stars: interiors Equation of state methods: analytical The isothermal gas sphere is a particular type of Lane-Emden equation and is used widely to model many problems in astrophysics, like the formation of stars, star clusters and galaxies. In this paper, we present a computational scheme to simulate the conformable fractional isothermal gas sphere using an artificial neural network (ANN) technique, and we compare the obtained results with the analytical solution deduced using the Taylor series. We performed our calculations, trained the ANN, and tested it using a wide range of the fractional parameter. Besides the Emden functions, we calculated the mass-radius relations and the density profiles of the fractional isothermal gas spheres. The results obtained show that the ANN could perfectly simulate the conformable fractional isothermal gas spheres. 2021 artículo científico 0185-1101 https://www.redalyc.org/articulo.oa?id=57171634014 https://www.redalyc.org/journal/571/57171634014/ https://www.redalyc.org/journal/571/57171634014/html/ https://www.redalyc.org/journal/571/57171634014/57171634014.epub https://www.redalyc.org/journal/571/57171634014/movil 10.22201/ia.01851101p.2021.57.01.14 en http://www.redalyc.org/revista.oa?id=571 Revista Mexicana de Astronomía y Astrofísica application/pdf Universidad Nacional Autónoma de México Revista Mexicana de Astronomía y Astrofísica (México) Num.1 Vol.57 |
| format | Artículo científico |
| id | redalyc_57171634014 |
| institution | Redalyc |
| language | en |
| publishDate | 2021 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Artificial Neural Network Modeling of the Conformable Fractional Isothermal Gas Spheres Yosry. A. Azzam Física, Astronomía y Matemáticas stars: neutron stars: interiors Equation of state methods: analytical Artificial Neural Network Modeling of the Conformable Fractional Isothermal Gas Spheres Yosry. A. Azzam Emad A.-B. Abdel-Salam Mohamed I. Nouh Física, Astronomía y Matemáticas stars: neutron stars: interiors Equation of state methods: analytical The isothermal gas sphere is a particular type of Lane-Emden equation and is used widely to model many problems in astrophysics, like the formation of stars, star clusters and galaxies. In this paper, we present a computational scheme to simulate the conformable fractional isothermal gas sphere using an artificial neural network (ANN) technique, and we compare the obtained results with the analytical solution deduced using the Taylor series. We performed our calculations, trained the ANN, and tested it using a wide range of the fractional parameter. Besides the Emden functions, we calculated the mass-radius relations and the density profiles of the fractional isothermal gas spheres. The results obtained show that the ANN could perfectly simulate the conformable fractional isothermal gas spheres. 2021 artículo científico 0185-1101 https://www.redalyc.org/articulo.oa?id=57171634014 https://www.redalyc.org/journal/571/57171634014/ https://www.redalyc.org/journal/571/57171634014/html/ https://www.redalyc.org/journal/571/57171634014/57171634014.epub https://www.redalyc.org/journal/571/57171634014/movil 10.22201/ia.01851101p.2021.57.01.14 en http://www.redalyc.org/revista.oa?id=571 Revista Mexicana de Astronomía y Astrofísica application/pdf Universidad Nacional Autónoma de México Revista Mexicana de Astronomía y Astrofísica (México) Num.1 Vol.57 |
| title | Artificial Neural Network Modeling of the Conformable Fractional Isothermal Gas Spheres |
| topic | Física, Astronomía y Matemáticas stars: neutron stars: interiors Equation of state methods: analytical |
| url | https://www.redalyc.org/articulo.oa?id=57171634014 https://www.redalyc.org/journal/571/57171634014/ https://www.redalyc.org/journal/571/57171634014/html/ https://www.redalyc.org/journal/571/57171634014/57171634014.epub https://www.redalyc.org/journal/571/57171634014/movil |