Solution of Differential Equations

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Numerical solution methods for fractional partial differential equations

Numerical solution methods for fractional partial differential equations

The numerical solution of fractional partial differential equations has been developed in several ways by using the Finite Difference method (Chen, Liu & Burrage 2008, Murio 2008, Hu & Zhang 2012, Sweilam, Khader & Mahdy 2012, Tadjeran 2007, Tadjeran & Meerschaert 2007), the Adomian Decomposition method (Dhaigude & Birajdar 2012, Di- ethelm & Ford 2002), the Predictor–Corrector method (Diethelm & Ford 2002), the Finite Element method (Deng 2008, Jiang & Ma 2013), and Numerical Quadrature (Diethelm 1997, Murio 2008). The majority of these numerical methods either use the Gr¨ unwald–Letnikov approximation or the L1 scheme to approximate the fractional deriva- tive. However, there are other techniques used to approximate the fractional derivative such as the Spline method (Pedas & Tamme 2011, Li 2012) and the Collocation method (Rawashdeh 2006, Hesameddini & Asadollahifard 2016).

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Stream differential equations : specification formats and solution methods

Stream differential equations : specification formats and solution methods

functions. For example, f (σ)(0) = σ(0), f (σ) ′ = f (σ ′′ ) (this time using second-order derivatives) defines the function f (σ) = (σ(0), σ(2), σ(4), . . . ). In these examples, it is easy to see that the stream differential equations have a unique solution. But how about τ (0) = 0, τ ′ = f (τ )? A moment’s thought reveals that this equation has several solutions, e.g. τ = (0, 0, 0, . . .) and τ = (0, 0, 1, 1, 1, . . .). But what is the difference between this equation and the previous ones? How can we ensure the existence of unique solutions? Which classes of streams can be defined using a finite amount of information? These questions have been studied by several authors in many different contexts in recent years, and have led to notions such as rational streams, context-free streams, and new insights into automatic and regular sequences.

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Numerical Solution of Differential Equations

Numerical Solution of Differential Equations

To study the techniques for solving differential equations based on numerical approximations were developed before programmable computers existed. Differential equations are an important part of many areas of mathematics, from fluid dynamics to celestial mechanics. Many mathematicians have studied the nature of these equations for hundreds of years and there are many well-developed solution techniques. Except for a few special cases, differential equations cannot normally be solved analytically. Instead, there are many numerical methods which have been developed to provide solutions. Keywords: Differential Equations, Fluid Dynamics, Numerical Method.

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A Meshless Method for Numerical Solution of Fractional Differential Equations

A Meshless Method for Numerical Solution of Fractional Differential Equations

methods have been used to solve fractional differential equations. There are some further method, such as operational method,the Adomian decomposition method(ADM)[6], the homotopy perturbation method(HPM)[7, 10], the generalized differential transformation method(GDTM)[9]. In this work, we approximate the exact solution by use of Radial Basis Functions method(RBFs).We present the advantages of using the RBFs especially where in the data points are scattered. Radial basis function(RBF) is one of the most popular basis for construction of meshless methods. It is (conditionally) positive defi- nite, rotationally and translationally invariant.Over the last 27 years, RBF methods have become an important tool for the interpolation of scattered data and for solving partial differential equations[10].

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Solution of fuzzy differential equations

Solution of fuzzy differential equations

Hybrid system is a dynamic system that exhibits both continuous and discrete dynamic behavior. The hybrid differential equations have a wide range of applications in science and engineering. The hybrid systems are devoted to modeling, design, and validation of interactive systems of computer programs and continuous systems. Hybrid fuzzy differential equations (HFDEs) is considered by Kim et al. [11]. In the present paper it is shown that the example presented by Kim et al. in the Case I is not very accurate and in the Case II, is incorrect. Namely, the exact solution proposed by the authors in the Case II are not solutions of the given HFDE. The correct exact solution is also presented here, together with some results for characterizing solutions of FDEs under Hukuhara differentiability by an equivalent system of ODEs. Then, the homotopy analysis method (HAM) is applied to obtained the series solution of the HFDEs. Finally, we illustrate our approach by a numerical example. Keywords : Fuzzy differential equations; Homotopy analysis method; Approximate solution.

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Numerical Solution of Differential Equations by Direct Taylor Expansion

Numerical Solution of Differential Equations by Direct Taylor Expansion

The objective of this article is to discuss a variation of the direct Taylor series (DTS) algorithm for the solution of first- and higher-order differential equations. We show that not only this algorithm remains accurate away from the initial point, evaluation of the higher derivatives that are needed for accuracies com- parable to the RK, ABM, and Milne methods are indeed quite simple. Finally, the accuracy and ease of application of the DTS method are explicitly demon- strated by considering several important second-order linear and nonlinear dif- ferential equations of mathematical physics and comparing their solutions using the fourth-order DTS, RK, ABM, and Milne methods.

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Approximate Solution of Fuzzy Fractional Differential Equations

Approximate Solution of Fuzzy Fractional Differential Equations

Fuzzy fractional differential equations have been introduced via general differentiability. Variational iteration method has been applied to obtain approximate fuzzy solution. Although the example given in this paper is a fuzzy fractional differential equation, it might also be applicable to fuzzy fractional partial differential equations.

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Analytical Exact Solution of Neural Functional differential Equations

Analytical Exact Solution of Neural Functional differential Equations

differential transform method (DTM) to solve the Neutral functional-differential equation with proportional delay. Firstly Zhou [21] introduced Differential transform Method on different type of nonlinear differential equations and has shown various remarkable results of this method. Using differential transformation method, a closed form series solution or an approximate solution can be obtained. The differential transform method obtains an analytical solution in the form of a polynomial. It is different from the traditional high order Taylor’s series method, which requires symbolic competition of the necessary derivatives of the data functions. The Taylor series method is computationally expansive for large orders in terms of time. This method produced solutions in the form of polynomials and avoids large computational work and round off error. In present time, much nonlinear type of ODEs is easily solved by DTM .This method has been successfully applied to solve many types of nonlinear problems in science and engineering [22-24]. Recently, many adaptive numerical methods have been used which are very effective for these problems [26-29]. In this work the proposed DTM method is analytically applied to Neutral functional-differential equation with proportional delays. Several examples are given to verify the efficiency and compatibility of the proposed method.

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Chebyshev approximation with applications to the numerical solution of differential equations

Chebyshev approximation with applications to the numerical solution of differential equations

Because x - 0 is not a point of maximum deviation of the optimal solution to the continuous problem, this solution is optimal on the wider range X £ x £ 2, where X s -0.76860078 (to eight figures). On this wider range, the maximum deviation is taken at three points, so that the problem is no longer s i n g u l a r However, the solution to the dual of the linear programming formulation of the discrete problem on the pornts of maximum deviation is still degenerate.

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Numerical Solution of First Order Ordinary Differential Equations

Numerical Solution of First Order Ordinary Differential Equations

In this article, we are presenting numerical solutions of first order differential equations arising in various applications of science and engineering using some classical numerical methods. We are considering only such practical problems which contain differential equations of the first order. Picard’s and Taylor series methods are used for solving such type of problems.

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Numerical Solution of First Order Ordinary Differential Equations

Numerical Solution of First Order Ordinary Differential Equations

Picard’s and Taylor’s series methods are powerful mathematical tools for solving linear and nonlinear differential equations. It is concluded that Picard’s and Taylor’s series methods gives more accurate solutions, which are much closer to exact solutions, for solving first order differential equations arising in some applications of sciences and engineering.

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An Algorithm for the Numerical Solution of System of Fractional Differential Equations

An Algorithm for the Numerical Solution of System of Fractional Differential Equations

derivatives, respectively. In section 5, we derive the fractional s method for the numerical solution of ordinary differential equations. The algorithm itself is presented in details in section 6. In section 7, we present three examples to show the efficiency and the simplicify of the algorithm .

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Existence and uniqueness theorem for a solution of fuzzy differential equations

Existence and uniqueness theorem for a solution of fuzzy differential equations

has a solution provided f is continuous and satisfies a Lipschitz condition by C. Corduneanu [2]. The definition given here generalizes that of Aumann [1] for set- valued mappings. Kaleva [3] discussed the properties of differentiable fuzzy set-valued mappings and gave the existence and uniqueness theorem for a solution of the fuzzy differential equation x (t) = f (t,x(t)) when f satisfies the Lipschitz condition. Also,

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The Lanczos Chebyshev Pseudospectral Method for Solution of Differential Equations

The Lanczos Chebyshev Pseudospectral Method for Solution of Differential Equations

In this paper, we propose to replace the Chebyshev series used in pseudospectral methods with the equivalent Chebyshev economized power series that can be evaluated more rapidly. We keep the rest of the implementation the same as the spectral method so that there is no new mathemat- ical principle involved. We show by numerical examples that the new approach works well and there is indeed no significant loss of solution accuracy. The advantages of using power series also include simplicity in its formulation and implementation such that it could be used for complex systems. We investigate the important issue of collocation point selection. Our numerical results indicate that there is a clear accuracy advantage of using collocation points corresponding to roots of the Chebyshev polynomial.

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Local Solution of Delay Fractional Differential Equations

Local Solution of Delay Fractional Differential Equations

 with d j as above is simply the power series representation of the Mittag-Leffer function E Lh n (  ) , and hence the required convergence of the series follows immediately. Therefore, we may apply Weissinger’s Fixed Point Theorem and deduce the uniqueness of the solution of our initial value problem (1-2)

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A New Approach In Determining Solution Of The Differential Equations And The First Order Partial Differential Equations

A New Approach In Determining Solution Of The Differential Equations And The First Order Partial Differential Equations

In this paper, we study the following methods. Murali Krishna’s method[1,2,3] for Non-Homogeneous First Order Differential Equations and formation of the differential equation by eliminating parameter in short methods. We consider z as dependent variables x and y are independent variables. The first order partial derivatives of z with respect to x and y are ∂x ∂z , ∂z ∂y which are denoted by p, q respectively. The equation d dx 2 y 2 + a 1 dy dx + a 2 y = X is called

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Generalized Differential Transformation Method for Solving System of Linear Volterra Integro-Differential Equations of Fractional Order

Generalized Differential Transformation Method for Solving System of Linear Volterra Integro-Differential Equations of Fractional Order

In this paper, the Generalized Differential Transformation Method (GDTM) for approximating the solution of systems of linear volterra integro-differential equations of fractional of fractional is implemented. The fractional derivative is considered in the Caputo sense. The approximate solutions are calculated in the form of a convergent series with easily quantifiable workings. Numerical results show that this approach is easy to implement and accurate when applied to systems integro-differential equations.

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Positive Solutions for Fractional Differential Equations with Multi Point Boundary Value Problems

Positive Solutions for Fractional Differential Equations with Multi Point Boundary Value Problems

It should be noted that most of papers and books on fractional calculus are devoted to the solvability of linear initial fractional differential equations on terms of special functions. Recently, there are some papers dealing with the existence and multiplicity of solution to the nonlinear fractional differential equations boundary value problems, see [8]-[14].

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Three Dimensional Analysis of Laminated Cylindrical Panels with Piezoelectric Layers

Three Dimensional Analysis of Laminated Cylindrical Panels with Piezoelectric Layers

Abstract A semi-analytical solution is presented for three dimensional elastic analysis of finitelylong, simply supported, orthotropic, laminated cylindrical panels with piezoelectric layers subjected to outer pressure and electrostatic excitation. Both the direct and inverse piezoelectric effects are investigated. The solution is obtained through reducing the highly coupled partial differential equations (PDE's) of equilibrium to ordinary differential equations (ODE's) with variable coefficients by means of trigonometric function expansion in longitudinal and circumferential directions. The resulting ODE's are solved by dividing the radial domain into some finite subdivisions and imposing necessary continuity conditions between the adjacent sub-layers. Some numerical examples are presented for the stress distribution and electric responses due to outer pressure in both sensorial and actuating states. Also, the effect of geometric properties on the sensitivity and actuating power of the structure are investigated.

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Solution Of Integro-Differential Equation Of The Second Order With The Operators

Solution Of Integro-Differential Equation Of The Second Order With The Operators

In this paper, we study the existence, uniqueness and stability solution of integro-differential equations of second order with the operators by using both method Picard approximation and Banach fixed point theorem.These investigations lead us to improving and extending the above method. . Thus the integro- differential equations of second order with the operators are more general and

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