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      A Comparative Analysis of Fractional-Order Kaup–Kupershmidt Equation within Different Operators

      , , , , ,
      Symmetry
      MDPI AG

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          Abstract

          In this paper, we find the solution of the fractional-order Kaup–Kupershmidt (KK) equation by implementing the natural decomposition method with the aid of two different fractional derivatives, namely the Atangana–Baleanu derivative in Caputo manner (ABC) and Caputo–Fabrizio (CF). When investigating capillary gravity waves and nonlinear dispersive waves, the KK equation is extremely important. To demonstrate the accuracy and efficiency of the proposed technique, we study the nonlinear fractional KK equation in three distinct cases. The results are given in the form of a series, which converges quickly. The numerical simulations are presented through tables to illustrate the validity of the suggested technique. Numerical simulations in terms of absolute error are performed to ensure that the proposed methodologies are trustworthy and accurate. The resulting solutions are graphically shown to ensure the applicability and validity of the algorithms under consideration. The results that we obtain confirm that the proposed method is the best tool for handling any nonlinear problems arising in science and technology.

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          Most cited references33

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          Global existence theory and chaos control of fractional differential equations

          Wei Lin (2007)
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            Analytical solution of a time-fractional Navier–Stokes equation by Adomian decomposition method

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              Fractional calculus and continuous-time finance

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                Author and article information

                Contributors
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                Journal
                SYMMAM
                Symmetry
                Symmetry
                MDPI AG
                2073-8994
                May 2022
                May 11 2022
                : 14
                : 5
                : 986
                Article
                10.3390/sym14050986
                a038fed1-8d73-4dee-8d1e-0259402872cd
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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