Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

Browse

Filters

2020 - 20232

Settings

Author: search.filters.author.Akgül, AliSubject: search.filters.subject.reproducing kernel Hilbert space method

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    An efficient approach for solving differential equations in the frame of a new fractional derivative operator
    (MDPI, 2023) Attia, Nourhane; Akgül, Ali; Seba, Djamila; Nour, Abdelkader; la Sen, Manuel De; Bayram, Mustafa
    Recently, a new fractional derivative operator has been introduced so that it presents the combination of the Riemann–Liouville integral and Caputo derivative. This paper aims to enhance the reproducing kernel Hilbert space method (RKHSM, for short) for solving certain fractional differential equations involving this new derivative. This is the first time that the application of the RKHSM is employed for solving some differential equations with the new operator. We illustrate the convergence analysis of the applicability and reliability of the suggested approaches. The results confirm that the RKHSM finds the true solution. Additionally, these numerical results indicate the effectiveness of the proposed method
  • No Thumbnail Available
    Item
    Reproducing kernel Hilbert space method for the numerical solutions of fractional cancer tumor models
    (2020) Attia, Nourhane; Akgül, Ali; Seba, Djamila; Nour, Abdelkader
    This research work is concerned with the new numerical solutions of some essential fractional cancer tumor models, which are investigated by using reproducing kernel Hilbert space method (RKHSM). The most valuable advantage of the RKHSM is its ease of use and its quick calculation to obtain the numerical solutions of the considered problem. We make use of the Caputo fractional derivative. Our main tools are reproducing kernel theory, some important Hilbert spaces, and a normal basis. We illustrate the high competency and capacity of the suggested approach through the convergence analysis. The computational results clearly show the superior performance of the RKHSM.