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    A new application of the reproducing kernel method for solving linear systems of fractional order Volterra integro-differential equations
    (IOP publishing Ltd, 2024) Amoozad, Taher; Abbasbandy, Saeid; Sahihi, Hussein; Allahviranloo, Tofigh
    In this article, a new implementation of the reproducing kernel method is presented for solving systems of fractional-order Volterra integro-differential equations. Unlike previous implementations, this method does not rely on the Gram-Schmidt process. The reproducing kernel method utilizes various components, including space, inner product, bases, and points. Furthermore, the system of fractional-order Volterra integro-differential equations involves Caputo's fractional derivative and Volterra integral. However, when using the reproducing kernel method to solve these systems, challenges such as longer execution time and lower accuracy may arise compared to other methods. The present method has overcome these challenges with features such as easy implementation, high accuracy, and lower execution time.
  • Küçük Resim Yok
    Öğe
    A new application of the reproducing kernel method for solving linear systems of fractional order Volterra integro-differential equations
    (Institute of Physics, 2024) Amoozad, Taher; Abbasbandy, Saeid; Sahihi, Hussein; Allahviranloo, Tofigh
    In this article, a new implementation of the reproducing kernel method is presented for solving systems of fractional-order Volterra integro-differential equations. Unlike previous implementations, this method does not rely on the Gram-Schmidt process. The reproducing kernel method utilizes various components, including space, inner product, bases, and points. Furthermore, the system of fractional-order Volterra integro-differential equations involves Caputo’s fractional derivative and Volterra integral. However, when using the reproducing kernel method to solve these systems, challenges such as longer execution time and lower accuracy may arise compared to other methods. The present method has overcome these challenges with features such as easy implementation, high accuracy, and lower execution time. © 2024 IOP Publishing Ltd.