Research Article | Open Access

Metomou Richard, Weidong Zhao, "Padé-Sumudu-Adomian Decomposition Method for Nonlinear Schrödinger Equation", *Journal of Applied Mathematics*, vol. 2021, Article ID 6626236, 19 pages, 2021. https://doi.org/10.1155/2021/6626236

# Padé-Sumudu-Adomian Decomposition Method for Nonlinear Schrödinger Equation

**Academic Editor:**Bruno Carpentieri

#### Abstract

The main purpose of this paper is to solve the nonlinear Schrödinger equation using some suitable analytical and numerical methods such as Sumudu transform, Adomian Decomposition Method (ADM), and Padé approximation technique. In many literatures, we can see the Sumudu Adomian decomposition method (SADM) and the Laplace Adomian decomposition method (LADM); the SADM and LADM provide similar results. The SADM and LADM methods have been applied to solve nonlinear PDE, but the solution has small convergence radius for some PDE. We perform the SADM solution by using the function called double Padé approximation. We will provide the graphical numerical simulations in 3D surface solutions of each application and the absolute error to illustrate the efficiency of the method. In our methods, the nonlinear terms are computed using Adomian polynomials, and the Padé approximation will be used to control the convergence of the series solutions. The suggested technique is successfully applied to nonlinear Schrödinger equations and proved to be highly accurate compared to the Sumudu Adomian decomposition method.

#### 1. Introduction

In science and engineering, most problems can be represented by linear or nonlinear ordinary or partial differential equations. To provide more important information about the scientific questions, it is necessary to solve these problems accurately. So to achieve this goal, various researches have been done to find more efficient solutions to these problems. Many analytical and numerical methods have been established. Different powerful mathematical techniques such as Adomian Decomposition Method (ADM) [1–3], Sumudu transform [4], Sumudu Adomian Decomposition method (SADM) [5–7], homotopy perturbation method (HPM), Laplace decomposition method (LDM) [8], Padé approximant [9–13], Laplace transform combined with Padé approximation [14], homotopy perturbation Sumudu transform method (HPSTM) [15], Homotopy Padé Approximate [16], Modified Adomian Decomposition Method [17–19], Homotopy Laplace transform [20], Akbari-Ganji’s Method (AGM) [21–23], and Hopf bifurcation [24] have been proposed to obtain exact and approximate analytic solutions. Inspired by many researchers in this field, we offer a new method called the Padé Sumudu Adomian decomposition method (PSADM) for solving the nonlinear Schrödinger equations in the present paper. We are mentioning that the proposed method is a combination of the Sumudu Adomian decomposition method and Padé approximation. Padé approximation has been applied for rational series solutions in many areas; the technique was developed around 1890 by Henri Padé [11]. The existence and the convergence of subsequences were proved in [12] by Baker. We have known that Padé approximants show high performance over series approximations, and the diagonal Padé approximation is bounded. The Padé approximation can be used to control the convergence of the series. It can be seen in many papers; the Padé approximants give better numerical results than approximation by the polynomial.

The Adomian Decomposition Method (ADM) was introduced in 1980 by George Adomian, a new robust method for solving a nonlinear functional equation. The technique has been applied to broad class of stochastic and deterministic problems in biology, physics, and chemical.

Wazwaz and Salah introduced the modified Adomian decomposition method; the series solution converges rapidly. The algorithm proposed by Wazwaz for calculating Adomian polynomials for all forms of nonlinearity is not easy to implement due to its massive size of algebraic calculations, complicated trigonometric terms.

Choi and Shin proposed a new symbolic implementation code [25]; this technique was a developed method proposed by Wazwaz. The decomposition method has been shown to solve easily, effectively, and accurately a large class of linear and nonlinear ordinary differential equation and has been applied to obtain formal solutions to a broad category of the partial differential equations of fractional orders, when initial and boundary conditions have been given. Abbaoui and Cherruault [26] proved the convergence of the Adomian method for differential and operator equations.

In the year 1990, the Sumudu transform was introduced by Gamage K. Watugala to solve differential equations and control engineering problems. The Sumudu transform is an integral transform similar to the Laplace transform; it is equivalent to the Laplace-Carson transforms [27, 28] with the substitution . In Sinhala language, the word Sumudu means “smooth.” The Sumudu transform method can only solve linear PDE. In many papers, we can see the modified Sumudu decomposition method [29], the Sumudu Adomian Decomposition Method (SADM) which is a combination of Sumudu transform and Adomian Decomposition Method. The method is applied to solve nonlinear PDE. For some PDE, the SADM solution is not accurate in large domain.

In this paper, we will apply our proposed method (PSADM) for solving the most common form of the nonlinear Schrödinger equation to approximate the solution numerically. These applications demonstrate the efficiency and show the advantage of PSADM to be more powerful than SADM.

#### 2. Preliminaries

##### 2.1. The Sumudu Transform

Sumudu transform was shown to have unit preserving properties and may be used to solve a frequency domain problem in engineering.

*Definition 1 [4]. *(Sumudu transform). The Sumudu transform is defined over the function set:
by the formula

By definition, the Sumudu transform is a linear operator, that is, where and are constants. About the Sumudu transform , the following theorems hold.

Theorem 2 (Sumudu theorems for multiple differentiations). *Let and let denote the Sumudu transform of the th-order derivative of . Then, for ,
*

Theorem 3 [4]. *Let us be the Sumudu transform of such that
*(i)* is a meromorphic function, with singularities having , and*(ii)*There exists a circular region with radius and positive constants, and , with
*

Then, the inverse of the Sumudu transform function of the function is given by:

Let us denote by the Sumudu transform with respect to and the inverse of the Sumudu transform with respect to . By using integral by part, we have the following theorem.

Theorem 4. *Assume is the Sumudu transform . Then, we have the relation
*

##### 2.2. The Adomian Decomposition Method ([1, 2])

George Adomian introduced a new powerful method known as the Adomian decomposition method (ADM) for solving nonlinear functional equations. The Adomian decomposition method has been applied to a various class of initial value or boundary value problems and even stochastic systems, such as ordinary-differential equations, partial-differential equations, integro-differential equations, fractional differential equations, and stochastic differential equations. The method has shown good results in supplying analytical approximation.

Let recall the basic principles of the Adomian decomposition method by solving the general nonlinear equation in the form where is the highest order differential in , is the highest order differential in , contains the remaining linear terms of lower order derivative terms, is the nonlinear term, and is the inhomogeneous or forcing term.

The decision as to choose which operator or should be used to solve the problem depends on two conditions: (i)The operator of lowest order should be selected to minimize the size of computational work(ii)The selected operator of lowest order should be the best known conditions to accelerate the evaluation of the components of the solution

Assuming that the operator satisfies the two bases of selection.

Take in both sides of (8) gives

The Adomian decomposition method consists in looking for the solution in the series form, . The nonlinear operator is decomposed as where are Adomian polynomials of which calculated by:

The first few Adomian polynomials are:

The decomposition method consists to identifying: for . After calculating , we obtain the series solution as

##### 2.3. The Padé Approximation

Padé approximation has been applied for rational series solutions in more papers. It is known that Padé approximants show high performance over series approximations. Padé approximants give better numerical results than approximation by the polynomial. The Padé approximate provides some advantage for controlling the convergences of approximation series.

A rational approximation of on is the quotient of two polynomials and of degrees and , respectively. We use the notation or to denote the quotient:

The idea of choosing the and is to make the maximum error as small as possible. For a given amount of computational effort, one can usually construct a rational approximation that has a smaller overall error on than a polynomial approximation. For sufficiently smooth function , we can properly choose the polynomials and such that the algebraic accuracy degree of the Padé approximation is .

*Definition 5. *Let be function of two variables and . We defined two-dimensional Padé approximation of the function as
where denote the -order Padé approximation of with respect to the variable , and denote the -order Padé approximation of with respect to the variable .

If , we will denote the diagonal Padé approximation of order by and called -order Padé approximation or M-Padé approximation of .

#### 3. SADM and PSADM for Solving Nonlinear PDEs

In this section, we take the following PDE problem as an example to show the Sumudu Adomian decomposition method (SADM) and the Padé Sumudu Adomian decomposition method (PSADM) for solving nonlinear PDE problems. with the initial condition , where is the first order differential in , is the highest order differential in , contains the remaining linear terms of lower order derivative terms, is the nonlinear term, and is the inhomogeneous or forcing term.

We write the procedures of SADM and PSADM for solving (17) as follows.

*Step 1. *Take the Sumudu transform to equation (17) to get

*Step 2. *Apply the inverse of the Sumudu transform to the above equation to obtain

*Step 3. *Use Adomian decomposition method to decomposite the nonlinear function and the , respectively, as

*Step 4. *Write the equation in the form
which lead to the SADM recursive relations:

*Step 5. *Deduct the SADM approximation solution :

*Step 6. *The -order PSADM solution is given by
if , we denote -PSADM solution by

#### 4. SADM and PSADM for Solving General Nonlinear Schrödinger Equation

Like Newton’s law in classical physics, the Schrödinger equation is the fundamental equation of quantum physics. It is used to describe the various problems in quantum optics, chemistry, atomic physics, biology, plasma physics, and recently in finance.

To illustrate the basic idea of this method, we consider a general nonlinear Schrödinger equation with the initial condition of the form: where represents the general nonlinear term and is a given analytical function.

Using the differentiation property of the Sumudu transform and above initial condition, we have

Let us represent the solution as the infinite series and the nonlinear term as where are Adomian polynomials of which is calculated by:

The general formula can be simplified. By using (30), for Adomian polynomials are given by

Other polynomials can be generated in a similar manner.

Then, we have the following algorithm for solving (26) and (27).

On comparing both sides, we let

After calculating the value of , we obtain the series solution as

Denote by , the Sumudu Adomian Decomposition Method solution is obtain by using , , , , ,…,

Then, the -order Padé Sumudu Adomian Decomposition Method solution is given by

##### 4.1. Application 1

Consider the Schrödinger equation of the form:

is real numbers, , is function of , and is analytical function.

Apply differentiation property of the Sumudu transform, we obtain:

Let us represent the solution as an infinite series given below

And the nonlinear term can be decomposed as:

For given where , is given by:

*Case 1. *, , and

Equation (38) becomes:

From (42) and (43), we obtain:

Then, we deduce the recursive relations:

Then, we obtain:

Accordingly, the series solution is given by

The algorithm is coded by the symbolic computation software Mathematica.

We know is the exact solution for the problem.

Figures 1(a), 1(c), and 1(e) show, respectively, the real part of Sumudu-Adomian-Decomposition Method solution , real part of Padé Sumudu-Adomian-Decomposition Method solution , and real part of exact solution in domain .

**(a) SADM:**

**(b) SADM:**

**(c) PSADM:**

**(d) PSADM:**

**(e) Exact:**

**(f) Exact:**

Figures 1(b), 1(d), and 1(f) show, respectively, the imaginary part of Sumudu-Adomian-Decomposition Method solution , imaginary part of Padé Sumudu-Adomian-Decomposition Method solution , and imaginary part of exact solution in domain .

Figures 2(a), 2(c), and 2(e) show, respectively, the real part of Sumudu-Adomian-Decomposition Method solution , real part of Padé Sumudu-Adomian-Decomposition Method solution , and real part of exact solution in domain .

**(a) SADM:**

**(b) SADM:**

**(c) PSADM:**

**(d) PSADM:**

**(e) Exact:**

**(f) Exact:**

Figures 2(b), 2(d), and 2(f) show, respectively, the imaginary part of Sumudu-Adomian-Decomposition Method solution , imaginary part of Padé Sumudu-Adomian-Decomposition Method solution , and imaginary part of exact solution in domain .

Figures 3(c) and 3(d) show, respectively, the absolute error for real part and imaginary part of the Padé Sumudu Adomian Decomposition solution .

**(a) SADM**

**(b) SADM**

**(c) PSADM**

**(d) PSADM**

Figures 3(a) and 3(b) show, respectively, the absolute error for real part and imaginary part of the Sumudu Adomian Decomposition solution .

We can see in this case the -order PSADM solution is better than SADM solution in domain , and both methods give almost same result in domain . The graph of absolute error shows that the PSADM solution is more efficient than SADM solution, and PSADM can be performed by choosing different value of .

*Case 2. *, , , and .

Equation (38) becomes: then

Accordingly, the series solution is given by

The algorithm is coded by the symbolic computation software Mathematica.

We know is the exact solution of the problem.

Figures 4(a), 4(c), and 4(e) show, respectively, the real part of Sumudu-Adomian-Decomposition Method solution , real part of Padé Sumudu-Adomian-Decomposition Method solution , and real part of exact solution in domain .

**(a) SADM:**

**(b) SADM:**

**(c) PSADM:**

**(d) PSADM:**

**(e) Exact:**

**(f) Exact:**

Figures 4(b), 4(d), and 4(f) show, respectively, the imaginary part of Sumudu-Adomian-Decomposition Method solution , imaginary part of Padé Sumudu-Adomian-Decomposition Method solution , and imaginary part of exact solution in domain .

Figures 4(a), 4(c), and 4(e) show, respectively, the real part of Sumudu-Adomian-Decomposition Method solution , real part of Padé Sumudu-Adomian-Decomposition Method solution , and real part of exact solution in domain . Figures 5(b), 5(d), and 5(f) show, respectively, the imaginary part of Sumudu-Adomian-Decomposition Method solution , imaginary part of Padé Sumudu-Adomian-Decomposition Method solution , and imaginary part of exact solution in domain .

**(a) SADM:**

**(b) SADM:**

**(c) PSADM:**

**(d) PSADM:**

**(e) Exact:**

**(f) Exact:**

Figures 6(c) and 6(d) show, respectively, the absolute error for real part and imaginary part of the Padé Sumudu Adomian Decomposition solution .

**(a) SADM**

**(b) SADM**