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Application of a Modified Adomian Decomposition Method for Solving Linear and Nonlinear Partial Differential Equations O., Okai J.; Musa, Abubakar; N., Sanda L.; M., Nasir U.; Y., Hafsat U.; S., Gidado A.; B, Mwaput D.; T., Danjuma; T., Shaukuna T.; Abdulkarim, Muhammad; U., Mujahid A.
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 3 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i3.7318

Abstract

Partial Differential Equations (PDEs) are fundamental to the mathematical modeling of various physical, chemical, and engineering phenomena. However, solving nonlinear PDEs poses significant challenges due to the lack of general closed-form solutions and the limitations of traditional numerical methods. This study introduces a Modified Adomian Decomposition Method (MADM) as an effective semi-analytical approach for solving both linear and nonlinear PDEs, with specific application to the Advection, Burgers’, and Sine-Gordon equations. The MADM enhances the classical Adomian Decomposition Method (ADM) by incorporating refined recursive structures and inverse operators, leading to improved solution accuracy and convergence speed. The results demonstrate that MADM not only yields highly accurate approximations but also reproduces exact solutions in certain cases. Comparative analysis with established methods such as the Variational Iteration Method (VIM) and the New Iteration Method (NIM) reveals that MADM outperforms them in terms of computational efficiency and precision. These findings underscore MADM's potential as a robust and efficient tool for solving a wide class of complex PDEs in applied sciences and engineering.
Application of a Modified Adomian Decomposition Method for Solving Linear and Nonlinear Partial Differential Equations O., Okai J.; Musa, Abubakar; N., Sanda L.; M., Nasir U.; Y., Hafsat U.; S., Gidado A.; B., Mwaput D.; T., Danjuma; T., Shaukuna T.; Abdulkarim, Muhammad; U., Mujahid A.
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 3 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i3.7492

Abstract

Partial Differential Equations (PDEs) are fundamental tools for modeling dynamic behaviors in physical, chemical, and engineering systems. However, solving nonlinear PDEs poses significant challenges due to the lack of closed-form solutions and the computational limitations of classical numerical approaches. This study introduces the Modified Adomian Decomposition Method (MADM) as an effective semi-analytical technique for solving both linear and nonlinear PDEs, with applications to the Advection, Burgers’, and Sine-Gordon equations. MADM enhances the classical Adomian Decomposition Method by incorporating refined recursive structures and inverse operators, which improve the convergence rate and simplify the solution process. The results demonstrate that MADM provides highly accurate solutions, often matching known exact solutions, and exhibits faster convergence compared to existing methods. Comparative analysis with the Variational Iteration Method (VIM) and the New Iteration Method (NIM) further highlights MADM’s computational efficiency and precision. These findings establish MADM as a robust and reliable tool for addressing complex PDEs across various scientific domains.
A Simplified Hybrid Analytical Method for Solving Integer and Fractional-Order Differential Equations without Adomian Polynomials or Lagrange Multipliers O., Okai J.; M., Cornelius; I., Abdulmalik; A., Jeremiah; M., Nasir U.; U., Hafsat Y.; O., Abichele
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 3 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i3.5719

Abstract

In this study, we propose a novel hybrid analytical technique that combines the Adomian Decomposition Method (ADM) with the Variational Iteration Method (VIM) to solve a class of linear and nonlinear first-order initial value problems (IVPs), including those of fractional order. The principal aim of this approach is to overcome the computational challenges typically encountered in each individual method—namely, the complexity of generating Adomian polynomials in ADM and the requirement for Lagrange multipliers in VIM. By synthesizing the strengths of both methods, the hybrid scheme constructs analytical series solutions without necessitating linearization, Adomian polynomials, or the explicit formulation of Lagrange multipliers. This significantly streamlines the solution process while preserving accuracy and generality. The validity and computational efficiency of the proposed method are substantiated through a series of illustrative examples, encompassing both integer-order and fractional differential equations. The results demonstrate that the hybrid approach not only simplifies implementation but also yields precise and rapidly converging solutions, making it a robust alternative for tackling a broad spectrum of initial value problems in mathematical modeling and applied sciences.
A Telescoping Decomposition Approach for Solving the Logistic Differential Equation O., Okai J.; Suzanna, Samson; N., Sanda L.; M., Nasir U.; Y., Hafsat U.; S., Gidado A.; B., Mwaput D.; T., Danjuma; U., Mujahid A.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 5 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i5.7296

Abstract

This paper investigates the application of the Telescoping Decomposition Method (TDM) to the Logistic Differential Equation (LDE) with the objective of obtaining accurate approximate solutions and benchmarking performance against established techniques. Methodologically, TDM is applied to two test cases, and the resulting approximations are compared with the exact solution and with those produced by the Elzaki Adomian Decomposition Method (EADM). The key findings show that TDM yields solutions in close agreement with the exact solution, with absolute errors reported as minimal (specific values not provided), and that it outperforms EADM in both accuracy and convergence rate while eliminating the need for repeated integral transforms. The study concludes that TDM is a simple, reliable, and computationally efficient approach for the LDE. The contribution and implication are that TDM offers a practical alternative for solving nonlinear differential equations and is readily extendable to more complex models.
A Hybrid Approach of the Variational Iteration Method and Adomian Decomposition Method for Solving Fractional Integro-Differential Equations O, Okai J.; Adamu, M. S.; M., Cornelius; I., Abdulmalik; A., Jeremiah; M., Nasir U.; U., Hafsat Y.; O., Abichele; Araga, Hassan
YASIN Vol 5 No 4 (2025): AGUSTUS
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/yasin.v5i4.5720

Abstract

In this study, we propose a hybrid analytical technique that integrates the Adomian Decomposition Method (ADM) with the Variational Iteration Method (VIM) to solve both linear and nonlinear integro-differential equations of integer and fractional orders. This approach extends and refines the Odibat Decomposition Method (ODM) by addressing key limitations inherent in ADM and VIM—specifically, the reliance on linearization, Adomian polynomials, and Lagrange multipliers. By circumventing these computational complexities, the proposed method enables the direct and efficient construction of series solutions with improved convergence properties. The hybrid scheme is designed for broader applicability and enhanced computational simplicity, making it a powerful tool for analyzing complex integro-differential systems. Its effectiveness and robustness are demonstrated through a range of illustrative examples, confirming the method’s capability to provide accurate analytical approximations with minimal computational overhead.