2021
DOI: 10.1088/1742-6596/1818/1/012182
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New Approach for Solving (2+1)-Dimensional Differential Equation

Abstract: This article presents an exact analysis solution for (2+1) dimensional differential equations by using new approach based on coupled method via LA-transform with decomposition method to overcome the computational difficulties. Convergence of series solution has been discussed with two illustrated examples, and the method has shown a high-precision, fast approach to solve non-linear (2+1) dimensional PDEs with initial conditions, there is no need any discretization of domain or assumption for a small parameter … Show more

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Cited by 6 publications
(4 citation statements)
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“…The linear unknown function ( ) ( ) can be decomposed by infinite series of components, illustrated in equation ( 4), and should be decomposed infinite series illustrated in equation (5).…”
Section: Description Of the Suggested Modificationmentioning
confidence: 99%
See 1 more Smart Citation
“…The linear unknown function ( ) ( ) can be decomposed by infinite series of components, illustrated in equation ( 4), and should be decomposed infinite series illustrated in equation (5).…”
Section: Description Of the Suggested Modificationmentioning
confidence: 99%
“…The systems of partial differential equations (PDEs) have been used to describe many important models in real life, such as contamination, distribution of shallow water, heat, wave's contamination, and the chemical reactiondistribution model [1][2][3][4]. The general ideas and key characteristics of these systems are generally applicable [5]. In recent years, many authors have focused on solving the nonlinear systems of PDEs using various methods, such as Homotopy analysis method (HAM) [6], variational iteration method (VIM) [7], differential transform method (DTM) [8], Homotopy ISSN: 0067-2904…”
Section: Introductionmentioning
confidence: 99%
“…So, many authors used ANN for solving ODEs, PDEs, integral equations and integro equations [35]. The authors proposed various design of ANNs depending on architectural of network: number of layers, number of nodes in each layers, partial or fully connected between layers and/or between nodes in layers, way of feeding the data forward or backward, or depending on training supervise or unsupervised learning [11]. Lee and kang [15] proposed Hopfield neural network for solving differential equations that is unsupervised learning.…”
Section: Introductionmentioning
confidence: 99%
“…They are used to describe many life models such as exponential growth, population growth of species or the change in investment return over time 3 , cooling and heating problems, bank interest, radioactive decay problems even flow problems in solving continuous compound interest problems, orthogonal trajectories 4 and also involving fluid mechanics problems, population or conservation biology 5 , circuit design, heat transfer, seismic waves 6 . They are used in specific fields such as, in the field of medicine, where modeling cancer growth or the spread of disease may be described as differential equations 7 .…”
Section: Introductionmentioning
confidence: 99%