AUTHOREA
Log in Sign Up Browse Preprints
LOG IN SIGN UP
Emad Abdel-salam
Emad Abdel-salam

Public Documents 2
Approximation of functions by Complex conformable derivative bases in Frechet spaces
Gamal HAssan
Emad Abdel-salam

Gamal HAssan

and 2 more

September 25, 2021
In the present paper the representation, in different domains, of analytic functions by complex conformable fractional derivative bases (CCFDB) and complex conformable fractional integral bases (CCFIB) in Frechet space are investigated . Theorems are proved to show that such representation is possible in closed disks, open disks, open regions surrounding closed disks, at the origin and for all entire functions. Also, some results concerning the growth order and type of CCFDB and CCFIB are determined. Moreover the T-property of CCFDB and CCFIB are dis- cussed. The obtained results recover some known results when alpha = 1. Finally, some applications to the CCFDB and CCFIB of Bernoulli, Euler, Bessel and Chebyshev polynomials have been studied.
Conformable Fractional Models of the Stellar Helium Burning via Artificial Neural Net...
Emad Abdel-salam
Mohamed NOUH

Emad Abdel-salam

and 2 more

November 23, 2020
The helium burning phase represents the second stage that the star used to consume nuclear fuel in its interior. In this stage, the three elements carbon, oxygen, and neon are synthesized. The present paper has two folds, the first is to develop an analytical solution to the system of the conformable fractional differential equations of the helium burning network, where we used for this purpose the series expansion method and obtained recurrence relations for the product abundances i.e. helium, carbon, oxygen, and neon. Using four different initial abundances, we calculated 44 gas models covering the range of the fractional parameter with step . We found that the effects of the fractional parameter on the product abundances are small which coincides with the results obtained by a previous study. Second, we introduced the mathematical model of the neural network (NN) and developed a neural network algorithm to simulate the helium burning network using its feed-forward model that is trained by the back propagation (BP) gradient descent delta rule algorithm. A comparison between the NN and the analytical models revealed very good agreement for all gas models. We found that NN could be considered as a powerful tool to solve and model nuclear burning networks and could be applied to the other nuclear stellar burning networks.

| Powered by Authorea.com

  • Home