Startseite Technik A new analytical method to simulate the mutual impact of space-time memory indices embedded in (1 + 2)-physical models
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A new analytical method to simulate the mutual impact of space-time memory indices embedded in (1 + 2)-physical models

  • Mohammad Makhadmih , Imad Jaradat EMAIL logo , Marwan Alquran und Dumitru Baleanu
Veröffentlicht/Copyright: 10. Oktober 2022
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Abstract

In the present article, we geometrically and analytically examine the mutual impact of space-time Caputo derivatives embedded in (1 + 2)-physical models. This has been accomplished by integrating the residual power series method (RPSM) with a new trivariate fractional power series representation that encompasses spatial and temporal Caputo derivative parameters. Theoretically, some results regarding the convergence and the error for the proposed adaptation have been established by virtue of the Riemann–Liouville fractional integral. Practically, the embedding of Schrödinger, telegraph, and Burgers’ equations into higher fractional space has been considered, and their solutions furnished by means of a rapidly convergent series that has ultimately a closed-form fractional function. The graphical analysis of the obtained solutions has shown that the solutions possess a homotopy mapping characteristic, in a topological sense, to reach the integer case solution where the Caputo derivative parameters behave similarly to the homotopy parameters. Altogether, the proposed technique exhibits a high accuracy and high rate of convergence.

MSC 2010: 26A33; 34A25; 35R11

1 Introduction

Fractional derivatives have proven their capability to describe several phenomena associated with memory or aftereffects due to their nonlocality property [1,2]. Such phenomena are commonplace in physical processes, biological structures, and cosmological phenomena. For instance, the fractional electrodiffusion equations have been successfully used to describe the transport processes of charge carriers in systems with a hierarchical structure [3], the fractional Cattaneo equations have been used to study the transport process of electrolytes in media where subdiffusion occurs [4], the fractional Kelvin–Voigt rheological models have been employed to examine the hydropolymer dynamics at low applied force frequencies [5], the fractional rheological model of the cell has been developed to study the relationship between the dynamic viscoelastic behavior of the cytoskeleton and the static contractile stress that it bears [6], the fractional rumor spreading dynamical model in a social network has been studied and analyzed in ref. [7], and several other fractional complex models have been utilized in turbulent [8], viscoelastic [9], kinetic and reaction–diffusion processes [10], and quantum mechanics [11].

For this reason, it became necessary to illuminate and find the solutions to the models that describe these phenomena. In this context, several numerical and analytical methods have been presented for solving hybrid models with fractional derivatives. Most of these approaches were accommodations for the existing methods of the integer case, which is considered a natural approach since the fractional derivative generalizes the classical derivative to an arbitrary order. Some of the most popular methods have been driven by Taylor’s power series method (TPSM) [12,13,14, 15,16], the Adomian decomposition method [17,18], the homotopy perturbation method [19,20,21], the q-homotopy analysis with Elzaki transform method [22,23], the reduced differential transform method (RDTM) [24,25,26], the spectral-collocation with quadratic and cubic B-splines [27,28,29, 30], the Laplace and Sumudu transform methods [31,32], and the variational iteration method [33]. Further, the existence and uniqueness analysis of the solution of some time-fractional models have been examined. See, for example, refs [34,35].

The functionality of the aforementioned methods is mainly to examine influences of either the space- or the time-fractional derivatives. In contrast, several notable studies have shown that the power-law memory can be ingrained in both the spatial and temporal coordinates [36]. Motivated by these facts, several techniques related to the celebrated Taylor’s series, namely, TPSM, RDTM, and residual power series method (RPSM), have been adapted to furnish the solutions of models endowed with spatial and temporal fractional derivatives [37,38,39, 40,41,42, 43,44,45, 46,47,48]. By proceeding in this direction, our motivation in this work is to present a new semi-analytical technique to simulate the mutual impact of space-time Caputo derivatives embedded in (1 + 2)-physical models. For this purpose, we will consider and adapt the RPSM by combining it with a new trivariate fractional power series that comprised spatial and temporal Caputo fractional derivatives and provide the necessary convergence and error analysis related to this adaptation. The proposed method will be called by ( α , β , γ ) -fractional residual power series method (FRPSM). Further, we will also provide a geometric interpretation for the role of the Caputo fractional derivative parameters. It should be noted here that the method’s applicability and efficiency require a high Caputo differentiability for the solution. In other words, the solution needs to be analytic in the sense of the Caputo fractional derivatives. We should mention here that all derivatives are defined in the Caputo sense due to their role in modeling phenomena with nonlocal properties and problems that possess interactions with the past [49].

It is worth mentioning here that the RPSM was first developed by a Jordanian researcher in ref. [50] to provide a series solution for the fuzzy differential equations under strongly generalized differentiability. In fact, the mechanism of the RPSM is a reformulation of the celebrated TPSM where the series coefficients are obtained by minimizing the residual error for the truncated series solution. This, in turn, implies that the series coefficients can be obtained by a successive differentiation of the truncated series solution. Recently, the RPSM has been successfully utilized to acquire approximate solutions for various problems in many areas [51,52,53, 54,55].

The remainder of this article is presented as follows. An adaptation of the RPSM for handling fractional embedding of (1 + 2)-physical models is presented in Section 2 along with some convergence and error results. In Section 3, the solution for the embedding of Schrödinger, telegraph, and Burgers’ equations has furnished by means of the proposed method. Finally, concluding remarks are presented in Section 4.

2 The methodology of ( α , β , γ ) -FRPSM

As mentioned earlier, our main goal is to combine the RPSM with a new trivariate power series expansion that is endowed with three Caputo derivative parameters α , β , γ ( 0 , 1 ) to study their mutual impact. We start this section by recalling the notion of ( α , β , γ ) -FPS and some of its relevant properties and convergence results.

Definition 2.1

[43]. An ( α , β , γ ) -fractional power series centered at the origin (simply, ( α , β , γ ) -fractional power series [FPS]) with constant coefficients { λ i , j , k } is a power series endowed with three fractional derivative parameters α , β , γ ( 0 , 1 ) in the following Cauchy form:

(2.1) i + j + k = 0 λ i , j , k t i α x j β y k γ = λ 0 , 0 , 0 i + j + k = 0 + λ 1 , 0 , 0 t α + λ 0 , 1 , 0 x β + λ 0 , 0 , 1 y γ i + j + k = 1 + + r = 0 n s = 0 r λ n r , r s , s t ( n r ) α x ( r s ) β y s γ i + j + k = n + ,

where i , j , k N and t , x , and y are nonnegative variables.

Proposition 2.2

[43]. If there exists t 0 , x 0 , y 0 R 0 such that { λ i , j , k t 0 i α x 0 j β y 0 k γ : i , j , k N } is a bounded set, then the ( α , β , γ ) -FPS is absolutely convergent on [ 0 , t 0 ) × [ 0 , x 0 ) × [ 0 , y 0 ) .

Theorem 2.3

[43]. The ( α , β , γ ) -FPS is either absolutely convergent for all ( t , x , y ) R 0 3 or there exists R t , R x , R y R 0 such that it is absolutely convergent on [ 0 , R t ) × [ 0 , R x ) × [ 0 , R y ) . Moreover, the set { λ i , j , k t i α x j β y k γ : ( i , j , k ) N 3 } is unbounded otherwise.

Definition 2.4

The triple ( R t , R x , R y ) R 0 3 in Theorem 2.3 is called the radius of convergence for ( α , β , γ ) -FPS. Otherwise, the radius of convergence is said to be infinite.

Remark 2.5

It is worth mentioning here that the ( α , β , γ ) -FPS can be rewritten as the following Cauchy product form:

(2.2) i = 0 j = 0 i k = 0 j λ i j , j k , k t ( i j ) α x ( j k ) β y k γ .

Theorem 2.6

[43]. Let i = 0 b i t i α , j = 0 c j x j β , and k = 0 d k y k γ be three absolutely convergent series at t 0 , x 0 , and y 0 > 0 , respectively. Then their Cauchy product (2.2) is absolutely convergent on D, where, in this case, λ i j , j k , k = a i j b j k c k . Moreover,

(2.3) i + j + k = 0 λ i , j , k t i α x j β y k γ = i = 0 a i t i α j = 0 b j x j β k = 0 c k y k γ .

Next, we recall some basic knowledge regarding the Caputo-fractional derivative and the Riemann–Liouville fractional integral operators that will be employed in this work. The Caputo time-fractional derivative of order α ( n 1 , n ] , n N is defined for an appropriate function u ( t , x , y ) by ref. [56]

(2.4) D t α [ u ( t , x , y ) ] = 1 Γ ( n α ) 0 t ( t τ ) n α 1 n u ( τ , x , y ) τ n d τ , α ( n 1 , n ) , t > 0 n u ( t , x , y ) t n , α = n , t > 0 .

With a direct implementation of (2.4) and using the integration by parts, we particularly obtain for α ( 0 , 1 )

(2.5) D t α [ t a ] = Γ ( a + 1 ) Γ ( a α + 1 ) t a α , a α , a R , t > 0 0 , a = 0 ,

which will be intensively used in this work to derive our main results.

Remark 2.7

We can enforce the Caputo-fractional derivative order α to be in ( 0 , 1 ) since D t α [ u ( t , x , y ) ] = D t α ( n 1 ) [ D t ( n 1 ) [ u ( t , x , y ) ] ] for any order α ( n 1 , n ) , n N .

The Riemann–Liouville time-fractional integral operator of order α ( n 1 , n ) , n N is defined for an appropriate function u ( t , x , y ) by

(2.6) J t α u ( t , x , y ) = 1 Γ ( α ) 0 t ( t τ ) α 1 u ( τ , x , y ) d τ .

It should be noted here that the Riemann–Liouville time-fractional integral operator is a right inverse for the Caputo time-fractional derivative operator but not a left inverse. More precisely, for α ( n 1 , n ) , n N , we have

(2.7) D t γ J t γ u ( t , x , y ) = u ( t , x , y ) ,

and

(2.8) J t α D t α u ( t , x , y ) = u ( t , x , y ) m = 0 n 1 t ( m ) u ( 0 + , x , y ) t m m ! .

Notation 2.8

For the sake of shortening the mathematical equations, we will denote Γ ( i α + 1 ) by Γ α ( i ) .

Now, presume that u ( t , x , y ) has an ( α , β ) -FPS representation with radius of convergence ( R t , R x , R y ) . Then the mixed Caputo-fractional derivatives of u ( t , x , y ) is given in ref. [43] by

(2.9) D t i α D x j β D y k γ [ u ( t , x , y ) ] = r + s + m = 0 λ r + i , s + j , m + k × Γ α ( r + i ) Γ β ( s + j ) Γ γ ( m + k ) Γ α ( r ) Γ β ( s ) Γ γ ( m ) t r α x s β y m γ .

Consequently, by plugging ( t , x , y ) = ( 0 , 0 , 0 ) into (2.9), we have the following form for the series coefficients in terms of the mixed Caputo-fractional derivatives:

(2.10) λ i , j , k = D t i α D x j β D y k γ [ u ( 0 , 0 , 0 ) ] Γ α ( i ) Γ β ( j ) Γ γ ( k ) ,

and, therefore,

(2.11) u ( t , x , y ) = i + j + k = 0 D t i α D x j β D y k γ [ u ( 0 , 0 , 0 ) ] Γ α ( i ) Γ β ( j ) Γ γ ( k ) t i α x j β y k γ .

The last representation of u ( t , x , y ) will be recognized as the ( α , β , γ ) -Maclaurin series due to its similarity with the celebrated classical Maclaurin series.

Next, to achieve our goal, we extend the mechanism of the RPSM into ( α , β , γ ) -fractional space. Consider the following embedding of differential equations in ( α , β , γ ) -fractional space

(2.12) Ψ ( u ( t , x , y ) , D t α u ( t , x , y ) , D x β u ( t , x , y ) , D y γ u ( t , x , y ) , ) = 0 .

Assume the existence of the solution in the form of ( α , β , γ ) -FPS with radius of convergence ( R t , R x , R y ) . Thus, the n th-truncated series of u is

(2.13) u n ( t , x , y ) = i + j + k = 0 n λ i , j , k t i α x j β y k γ ,

and an approximate solution of (2.12) will be obtained when the coefficients λ i , j , k are determined for all permutations i + j + k = 0 , 1 , 2 , , n .

We define the residual function for the solution of Eq. (2.12) by

(2.14) Res u ( t , x , y ) = Ψ ( u ( t , x , y ) , D t α u ( t , x , y ) , D x β u ( t , x , y ) , D y γ u ( t , x , y ) , ) ,

and the residual function for the n th-truncated series solution of u by

(2.15) Res u n ( t , x , y ) = Ψ ( u n ( t , x , y ) , D t α u n ( t , x , y ) , D x β u n ( t , x , y ) , D y γ u n ( t , x , y ) , ) .

Since u is a solution of (2.12) and the Caputo-fractional derivative of a constant function is zero, then we have the following apparent properties:

(2.16) ( a ) Res u ( t , x , y ) = 0 ,

(2.17) ( b ) lim n Res u n ( t , x , y ) = Res u ( t , x , y ) , ( t , x , y ) ( 0 , R t ) × ( 0 , R x ) × ( 0 , R y ) ,

(2.18) ( c ) D t i α D x j β D y k γ [ Res u n ( t , x , y ) ] = 0 , for each i + j + k = 0 , 1 , 2 , , n .

Therefore, by inserting (2.13) into (2.15) and solving the resultant system of the following algebraic equations:

(2.19) D t i α D x j β D y k γ [ Res u n ( 0 , 0 , 0 ) ] = 0 ,

throughout all the permutations of i + j + k = n , n N , and we obtain the wanted coefficients λ i , j , k .

Next, we provide a formula for the remainder (or the error term) of the ( α , β , γ ) -Maclaurin series solution in terms of the Riemann–Liouville fractional integral:

Theorem 2.9

Let D t i α D x j β D y k γ [ u ( t , x , y ) ] C ( ( 0 , R t ) × ( 0 , R x ) × ( 0 , R y ) ) for each i + j + k = 0 , 1 , 2 , , n + 1 . Then u ( t , x , y ) can be expressed as follows

(2.20) u ( t , x , y ) = i + j + k = 0 n D t i α D x j β D y k γ [ u ( 0 , 0 , 0 ) ] Γ α ( i ) Γ β ( j ) Γ γ ( k ) × t i α x j β y k γ + R n ( t , x , y ) ,

where R n ( t , x , y ) is the remainder given in terms of the Riemann–Liouville fractional integral as follows

(2.21) R n ( t , x , y ) = j + k = 0 n J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α J x j β D x j β × J y k γ D y k γ [ u ( t , 0 , 0 ) ] + k = 0 n J x ( n + 1 k ) β D x ( n + 1 k ) β J y k γ D y k γ × [ u ( t , x , 0 ) ] + J y ( n + 1 ) γ D y ( n + 1 ) γ [ u ( t , x , y ) ] .

Proof

First, by the help of Eq. (2.8), one can show by the mathematical induction, as in [16, Lemma 3.1], that

(2.22) J y ( n + 1 ) γ D y ( n + 1 ) γ [ u ( t , x , y ) ] = u ( t , x , y ) k = 0 n D y k γ [ u ( t , x , 0 ) ] Γ γ ( k ) y k γ .

Using the fact that for a constant function c ,

(2.23) J y k γ c = c Γ γ ( k ) y k γ ,

and the sense of Eq. (2.22) with respect to t , we have

(2.24) j + k = 0 n J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α J x j β D x j β J y k γ D y k γ [ u ( t , 0 , 0 ) ] = j + k = 0 n J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α J x j β D x j β × D y k γ [ u ( t , 0 , 0 ) ] Γ γ ( k ) y k γ = j + k = 0 n J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α × D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ = j + k = 0 n D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ i = 0 n ( j + k ) D t i α D x j β D y k γ [ u ( 0 , 0 , 0 ) ] Γ α ( i ) Γ β ( j ) Γ γ ( k ) t i α x j β y k γ = j + k = 0 n D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ j + k = 0 n i = 0 n ( j + k ) D t i α D x j β D y k γ [ u ( 0 , 0 , 0 ) ] Γ α ( i ) Γ β ( j ) Γ γ ( k ) t i α x j β y k γ = j + k = 0 n D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ i + j + k = 0 n D t i α D x j β D y k γ [ u ( 0 , 0 , 0 ) ] Γ α ( i ) Γ β ( j ) Γ γ ( k ) t i α x j β y k γ = j + k = 0 n D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ ( u ( t , x , y ) R n ( t , x , y ) ) .

Similarly,

(2.25) k = 0 n J x ( n + 1 k ) β D x ( n + 1 k ) β J y k γ D y k γ [ u ( t , x , 0 ) ] = k = 0 n J x ( n + 1 k ) β D x ( n + 1 k ) β D y k γ [ u ( t , x , 0 ) ] Γ γ ( k ) y k γ = k = 0 n D y k γ [ u ( t , x , 0 ) ] Γ γ ( k ) y k γ j = 0 n k D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ = k = 0 n D y k γ [ u ( t , x , 0 ) ] Γ γ ( k ) y k γ k = 0 n j = 0 n k D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ = k = 0 n D y k γ [ u ( t , x , 0 ) ] Γ γ ( k ) y k γ j + k = 0 n D x j β D y k γ [ u ( t , 0 , 0 ) ] Γ β ( j ) Γ γ ( k ) x j β y k γ .

By adding Eqs. (2.22), (2.24), and (2.25) we obtain the required formula of R n ( t , x , y ) .

Theorem 2.10

Suppose that D t i α D x j β D y k γ [ u ( t , x , y ) ] C ( ( 0 , R t ) × ( 0 , R x ) × ( 0 , R y ) ) and there exists M > 0 such that D t i α D x j β D y k γ [ u ( t , x , y ) ] M for all permutations i + j + k = 0 , 1 , 2 , , n + 1 in D ˜ [ 0 , R t ) × [ 0 , R x ) × [ 0 , R y ) . Then

  1. the solution

    (2.26) u ( t , x , y ) = i + j + k = 0 D t i α D x j β D y k γ [ u ( 0 , 0 , 0 ) ] Γ α ( i ) Γ β ( j ) Γ γ ( k ) t i α x j β y k γ ,

    is absolutely convergent on D ˜ .

  2. for all ( t , x , y ) D ˜ , the remainder R n ( t , x , y ) satisfies the bound

    (2.27) R n ( t , x , y ) M i + j + k = n + 1 t i α x j β y k γ Γ α ( i ) Γ β ( j ) Γ γ ( k ) .

Proof

The proof of (a) follows directly from 2.2 and 2.3. For (b), from the remainder definition, we have D t i α D x j β D y k γ [ R n ( 0 , 0 , 0 ) ] = 0 for each i + j + k = 0 , 1 , 2 , , n and D t i α D x j β D y k γ [ R n ( t , x , y ) ] = D t i α D x j β D y k γ [ u ( t , x , y ) ] for all i + j + k n + 1 and ( t , x , y ) D ˜ . By assumption, for each i + j + k = n + 1 and ( t , x , y ) D ˜ , we have

M D t i α D x j β D y k γ [ u ( t , x , y ) ] M .

Therefore, for all ( t , x , y ) D ˜ ,

(2.28) i + j + k = n + 1 J y k γ J x j β J t i α [ M ] i + j + k = n + 1 J y k γ J x j β J t i α [ D t i α D x j β D y k γ [ u ( t , x , y ) ] ] i + j + k = n + 1 J y k γ J x j β J t i α [ M ] .

Since J y k γ J x j β J t i α [ M ] = M t i α x j β y k γ Γ α ( i ) Γ β ( j ) Γ γ ( k ) for all i + j + k = n + 1 , then

(2.29) i + j + k = n + 1 M t i α x j β y k γ Γ α ( i ) Γ β ( j ) Γ γ ( k ) i + j + k = n + 1 J y k γ J x j β J t i α [ D t i α D x j β D y k γ [ u ( t , x , y ) ] ] i + j + k = n + 1 M t i α x j β y k γ Γ α ( i ) Γ β ( j ) Γ γ ( k ) .

Thus,

(2.30) i + j + k = n + 1 J y k γ J x j β J t i α [ D t i α D x j β D y k γ [ u ( t , x , y ) ] ] M i + j + k = n + 1 t i α x j β y k γ Γ α ( i ) Γ β ( j ) Γ γ ( k ) .

Now, from Definition 2.1, the term i + j + k = n + 1 in the aforementioned left sum can be rewritten for all ( t , x , y ) D ˜ as follows:

(2.31) i + j + k = n + 1 J y k γ J x j β J t i α D t i α D x j β D y k γ [ u ( t , x , y ) ] = j + k = 0 n + 1 J y k γ J x j β J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α D x j β × D y k γ [ u ( t , x , y ) ] = j + k = 0 n J y k γ J x j β J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α D x j β D y k γ [ u ( t , x , y ) ] + j + k = n + 1 J y k γ J x j β D x j β × D y k γ [ u ( t , x , y ) ] = j + k = 0 n J y k γ J x j β J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α D x j β × D y k γ [ u ( t , x , y ) ] + k = 0 n J x ( n + 1 k ) β D x ( n + 1 k ) β J y k γ D y k γ [ u ( t , x , y ) ] + J y ( n + 1 ) γ × D y ( n + 1 ) γ [ u ( t , x , y ) ] .

Therefore, the inequality (2.30) becomes

(2.32) j + k = 0 n J y k γ J x j β J t ( n + 1 ( j + k ) ) α D t ( n + 1 ( j + k ) ) α D x j β × D y k γ [ u ( t , x , y ) ] + k = 0 n J x ( n + 1 k ) β D x ( n + 1 k ) β J y k γ × D y k γ [ u ( t , x , y ) ] + J y ( n + 1 ) γ D y ( n + 1 ) γ [ u ( t , x , y ) ] M i + j + k = n + 1 t i α x j β y k γ Γ α ( i ) Γ β ( j ) Γ γ ( k ) .

Since (2.32) is valid for all ( t , x , y ) D ˜ , in particular, we have the desired bound (2.27) of the remainder.

3 Application models

In this section, the declared ( α , β , γ ) -FRPSM will be employed to offer the solutions of the ( α , β , γ ) -embedding of Schrödinger and telegraph equations. These solutions will be presented in terms of a rapidly convergent series of the form ( α , β , γ ) -Maclaurin series, which eventually will have closed-form fractional functions. In addition, a graphical analysis has been provided to study the behavior of the solutions when the fractional derivative parameters vary in the interval ( 0 , 1 ) . In all our applications, we assume that the fractional derivative parameters α , β , γ ( 0 , 1 ) .

Application 1

Consider the following ( α , β , γ ) -Schrödinger problem:

(3.1) i D t α [ u ( t , x , y ) ] = D x 2 β [ u ( t , x , y ) ] + D y 2 γ [ u ( t , x , y ) ] ,

with initial condition

(3.2) u ( 0 , x , y ) = sin β ( x β ) + sin γ ( y γ ) ,

where sin β ( x β ) = j = 0 ( 1 ) j x ( 2 j + 1 ) β Γ β ( 2 j + 1 ) . We assume the existence of a solution for (3.1)–(3.2) in the form ( α , β , γ ) -FPS. To find the coefficients { λ i , j , k } i + j + k = 0 by our proposed method, we first construct the residual function for the n th-truncated series solution of Eq. (3.1):

(3.3) Res u n ( t , x , y ) = i + j + k = 0 n i λ i + 1 , j , k Γ α ( i + 1 ) Γ α ( i ) t i α x j β y k γ i + j + k = 0 n λ i , j + 2 , k Γ β ( j + 2 ) Γ β ( j ) t i α x j β y k γ i + j + k = 0 n λ i , j , k + 2 Γ γ ( k + 2 ) Γ γ ( k ) t i α x j β y k γ .

From the fractional initial condition (3.2), we have the initial coefficients for j , k 0

(3.4) λ 0 , 2 j + 1 , 0 = ( 1 ) j Γ β ( 2 j + 1 ) , λ 0 , 0 , 2 k + 1 = ( 1 ) k Γ γ ( 2 k + 1 ) , λ 0 , j , k = 0 , otherwise .

Next, we solve the system of algebraic equations { D t i α D x j β D y k γ [ Res u n ( 0 , 0 , 0 ) ] = 0 } for each i + j + k = n N with taking into consideration the initial coefficients (3.4), that is, by solving the following system:

When n = 0 , the system { D t i α D x j β D y k γ [ Res u 0 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 0 yields:

(3.5) i Γ α ( 1 ) λ 1 , 0 , 0 Γ β ( 2 ) λ 0 , 2 , 0 Γ γ ( 2 ) λ 0 , 0 , 2 = 0 .

When n = 1 , the system { D t i α D x j β D y k γ [ Res u 1 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 1 yields:

(3.6) i Γ α ( 2 ) λ 2 , 0 , 0 Γ α ( 1 ) Γ β ( 2 ) λ 1 , 2 , 0 Γ α ( 1 ) Γ γ ( 2 ) λ 1 , 0 , 2 = 0 , i Γ α ( 1 ) Γ β ( 1 ) λ 1 , 1 , 0 Γ β ( 3 ) λ 0 , 3 , 0 Γ β ( 1 ) Γ γ ( 2 ) λ 0 , 1 , 2 = 0 , i Γ α ( 1 ) Γ γ ( 1 ) λ 1 , 0 , 1 Γ β ( 2 ) Γ γ ( 1 ) λ 0 , 2 , 1 Γ γ ( 3 ) λ 0 , 0 , 3 = 0 .

When n = 2 , the system { D t i α D x j β D y k γ [ Res u 2 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 2 yields:

(3.7) i Γ α ( 3 ) λ 3 , 0 , 0 Γ α ( 2 ) Γ β ( 2 ) λ 2 , 2 , 0 Γ α ( 2 ) Γ γ ( 2 ) λ 2 , 0 , 2 = 0 , i Γ α ( 2 ) Γ β ( 1 ) λ 2 , 1 , 0 Γ α ( 1 ) Γ β ( 3 ) λ 1 , 3 , 0 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 2 ) λ 1 , 1 , 2 = 0 , i Γ α ( 2 ) Γ γ ( 1 ) λ 2 , 0 , 1 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 1 ) λ 1 , 2 , 1 Γ α ( 1 ) Γ γ ( 3 ) λ 1 , 0 , 3 = 0 , i Γ α ( 1 ) Γ β ( 2 ) λ 1 , 2 , 0 Γ β ( 4 ) λ 0 , 4 , 0 Γ β ( 2 ) Γ γ ( 2 ) λ 0 , 2 , 2 = 0 , i Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 1 ) λ 1 , 1 , 1 Γ β ( 3 ) Γ γ ( 1 ) λ 0 , 3 , 1 Γ β ( 1 ) Γ γ ( 3 ) λ 0 , 1 , 3 = 0 , i Γ α ( 1 ) Γ γ ( 2 ) λ 1 , 0 , 2 Γ β ( 2 ) Γ γ ( 2 ) λ 0 , 2 , 2 Γ γ ( 4 ) λ 0 , 0 , 4 = 0 .

When n = 3 , the system { D t i α D x j β D y k γ [ Res u 3 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 3 yields:

(3.8) i Γ α ( 4 ) λ 4 , 0 , 0 Γ α ( 3 ) Γ β ( 2 ) λ 3 , 2 , 0 Γ α ( 3 ) Γ γ ( 2 ) λ 3 , 0 , 2 = 0 , i Γ α ( 3 ) Γ β ( 1 ) λ 3 , 1 , 0 Γ α ( 2 ) Γ β ( 3 ) λ 2 , 3 , 0 Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 2 ) λ 2 , 1 , 2 = 0 , i Γ α ( 3 ) Γ γ ( 1 ) λ 3 , 0 , 1 Γ α ( 2 ) Γ β ( 2 ) Γ γ ( 1 ) λ 2 , 2 , 1 Γ α ( 2 ) Γ γ ( 3 ) λ 2 , 0 , 3 = 0 , i Γ α ( 2 ) Γ β ( 2 ) λ 2 , 2 , 0 Γ α ( 1 ) Γ β ( 4 ) λ 1 , 4 , 0 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 2 ) λ 1 , 2 , 2 = 0 , i Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 1 ) λ 2 , 1 , 1 Γ α ( 1 ) Γ β ( 3 ) Γ γ ( 1 ) λ 1 , 3 , 1 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 3 ) λ 1 , 1 , 3 = 0 , i Γ α ( 2 ) Γ γ ( 2 ) λ 2 , 0 , 2 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 2 ) λ 1 , 2 , 2 Γ α ( 1 ) Γ γ ( 4 ) λ 1 , 0 , 4 = 0 , i Γ α ( 1 ) Γ β ( 3 ) λ 1 , 3 , 0 Γ β ( 5 ) λ 0 , 5 , 0 Γ β ( 3 ) Γ γ ( 2 ) λ 0 , 3 , 2 = 0 , i Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 1 ) λ 1 , 2 , 1 Γ β ( 4 ) Γ γ ( 1 ) λ 0 , 4 , 1 Γ β ( 2 ) Γ γ ( 3 ) λ 0 , 2 , 3 = 0 , i Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 2 ) λ 1 , 1 , 2 Γ β ( 3 ) Γ γ ( 2 ) λ 0 , 3 , 2 Γ β ( 1 ) Γ γ ( 4 ) λ 0 , 1 , 4 = 0 , i Γ α ( 1 ) Γ γ ( 3 ) λ 1 , 0 , 3 Γ β ( 2 ) Γ γ ( 3 ) λ 0 , 2 , 3 Γ γ ( 5 ) λ 0 , 0 , 5 = 0 ,

and so forth. Solving the aforementioned sets of linear equations recursively leads to:

(3.9) λ 1 , 0 , 0 = 0 , λ 2 , 0 , 0 = 0 , λ 3 , 0 , 0 = 0 , λ 1 , 1 , 0 = i Γ α ( 1 ) Γ β ( 1 ) , λ 2 , 1 , 0 = i 2 Γ α ( 2 ) Γ β ( 1 ) , λ 3 , 1 , 0 = i 3 Γ α ( 3 ) Γ β ( 1 ) , λ 1 , 0 , 1 = i Γ α ( 1 ) Γ γ ( 1 ) , λ 2 , 0 , 1 = i 2 Γ α ( 2 ) Γ γ ( 1 ) , λ 3 , 0 , 1 = i 3 Γ α ( 3 ) Γ γ ( 1 ) , λ 1 , 2 , 0 = 0 , λ 2 , 2 , 0 = 0 , λ 3 , 2 , 0 = 0 , λ 1 , 0 , 2 = 0 , λ 2 , 0 , 2 = 0 , λ 3 , 0 , 2 = 0 . λ 1 , 1 , 1 = 0 , λ 2 , 1 , 1 = 0 , λ 1 , 3 , 0 = i Γ α ( 1 ) Γ β ( 3 ) , λ 2 , 3 , 0 = i 2 Γ α ( 2 ) Γ β ( 3 ) , λ 1 , 0 , 3 = i Γ α ( 1 ) Γ γ ( 3 ) . λ 2 , 0 , 3 = i 2 Γ α ( 2 ) Γ γ ( 3 ) .

We continue in this fashion until we obtain the rest of all coefficients. In general,

(3.10) λ i , 2 j + 1 , 0 = ( 1 ) j ( i ) i Γ α ( i ) Γ β ( 2 j + 1 ) , λ i , 0 , 2 k + 1 = ( 1 ) k ( i ) i Γ α ( i ) Γ γ ( 2 k + 1 ) , λ 0 , j , k = 0 , otherwise .

Compensating (3.10) in (2.1) and using Theorem 2.6, to obtain the following closed-form solution to the ( α , β , γ ) -Schrödinger problem:

(3.11) u ( t , x , y ) = i + j + k = 0 λ i , 2 j + 1 , 0 t i α x ( 2 j + 1 ) β + i + j + k = 0 λ i , 0 , 2 k + 1 t i α y ( 2 k + 1 ) γ = i + j + k = 0 ( 1 ) j ( i ) i Γ α ( i ) Γ β ( 2 j + 1 ) t i α x ( 2 j + 1 ) β + i + j + k = 0 ( 1 ) k ( i ) i Γ α ( i ) Γ γ ( 2 k + 1 ) t i α y ( 2 k + 1 ) γ = i = 0 ( i t α ) i Γ α ( i ) j = 0 ( 1 ) j x ( 2 j + 1 ) β Γ β ( 2 j + 1 ) + k = 0 ( 1 ) k y ( 2 k + 1 ) γ Γ γ ( 2 k + 1 ) = E α ( i t α ) ( sin β ( x β ) + sin γ ( y γ ) ) .

It is worth mentioning here that when α , β , γ 1 , we obtain the closed-form solution u ( t , x , y ) = e i t ( sin ( x ) + sin ( y ) ) for the classical Schrödinger model.

Figure 1 exhibits the behavior of some cross-sections of the 10th-approximate series solution u 10 ( t , x , y ) of the Eq. (3.11) at various values of α , β , γ ( 0 , 1 ) . Because of the solution symmetry, the cross-sections, when γ varies, are similar to the case of β . In all cases, it is evident that the cross-sections form a continuous sequence, as long as the fractional derivative parameter approaching 1, to reach the cross-section for the integer case solution. In other words, the solution (3.11) behaves like the homotopic mapping in the topological sense.

Figure 1 
               The cross-section behavior of 
                     
                        
                        
                           
                              
                                 u
                              
                              
                                 10
                              
                           
                           
                              (
                              
                                 t
                                 ,
                                 x
                                 ,
                                 y
                              
                              )
                           
                        
                        {u}_{10}\left(t,x,y)
                     
                   for (3.11) at various values of 
                     
                        
                        
                           α
                           ,
                           β
                           ,
                           γ
                           ∈
                        
                        \alpha ,\beta ,\gamma \in 
                     
                   (0,1). (a) 
                     
                        
                        
                           β
                           =
                           γ
                           =
                           1
                        
                        \beta =\gamma =1
                     
                  . (b) 
                     
                        
                        
                           α
                           =
                           γ
                           =
                           1
                        
                        \alpha =\gamma =1
                     
                  .
Figure 1

The cross-section behavior of u 10 ( t , x , y ) for (3.11) at various values of α , β , γ (0,1). (a) β = γ = 1 . (b) α = γ = 1 .

Application 2

Consider the following hyperbolic ( α , β , γ ) -telegraph problem:

(3.12) D t 2 α [ u ( t , x , y ) ] + 2 D t α [ u ( t , x , y ) ] + u ( t , x , y ) = 1 2 ( D x 2 β [ u ( t , x , y ) ] + D y 2 γ [ u ( t , x , y ) ] ) ,

with initial conditions

(3.13) u ( 0 , x , y ) = sinh β ( x β ) sinh γ ( y γ ) , D t α [ u ( 0 , x , y ) ] = 2 sinh β ( x β ) sinh γ ( y γ ) .

Again, we assume the existence of a solution for (3.12) and (3.13) in the form ( α , β , γ ) -FPS. To find the coefficients { λ i , j , k } i + j + k = 0 by our proposed method, we first construct the residual function for the n th-truncated series solution of Eq. (3.12) as follows:

(3.14) Res u n ( t , x , y ) = i + j + k = 0 n λ i + 2 , j , k Γ α ( i + 2 ) Γ α ( i ) t i α x j β y k γ + 2 i + j + k = 0 n λ i + 1 , j , k Γ α ( i + 1 ) Γ α ( i ) t i α x j β y k γ + i + j + k = 0 n λ i , j , k t i α x j β y k γ 1 2 i + j + k = 0 n λ i , j + 2 , k Γ β ( j + 2 ) Γ β ( j ) t i α x j β y k γ 1 2 i + j + k = 0 n λ i , j , k + 2 Γ γ ( k + 2 ) Γ γ ( k ) t i α x j β y k γ .

From the fractional initial condition (3.13), we have the initial coefficients for j , k 0

(3.15) λ 0 , 2 j + 1 , 2 k + 1 = 1 Γ β ( 2 j ) Γ γ ( 2 k ) , λ 1 , 2 j + 1 , 2 k + 1 = 2 Γ α ( 1 ) Γ β ( 2 j ) Γ γ ( 2 k ) , λ 0 , j , k = λ 1 , j , k = 0 , otherwise .

Next, we solve the system of algebraic equations { D t i α D x j β D y k γ [ Res u n ( 0 , 0 , 0 ) ] = 0 } for each i + j + k = n N with taking into consideration the initial coefficients (3.15). That is, solving the following system:

When n = 0 , the system { D t i α D x j β D y k γ [ Res u 0 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 0 yields:

(3.16) Γ α ( 2 ) λ 2 , 0 , 0 + 2 Γ α ( 1 ) λ 1 , 0 , 0 + λ 0 , 0 , 0 1 2 Γ β ( 2 ) λ 0 , 2 , 0 1 2 Γ γ ( 2 ) λ 0 , 0 , 2 = 0 .

When n = 1 , the system { D t i α D x j β D y k γ [ Res u 1 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 1 yields:

(3.17) Γ α ( 3 ) λ 3 , 0 , 0 + 2 Γ α ( 2 ) λ 2 , 0 , 0 + Γ α ( 1 ) λ 1 , 0 , 0 1 2 Γ α ( 1 ) Γ β ( 2 ) λ 1 , 2 , 0 1 2 Γ α ( 1 ) Γ γ ( 2 ) λ 1 , 0 , 2 = 0 , Γ α ( 2 ) Γ β ( 1 ) λ 2 , 1 , 0 + 2 Γ α ( 1 ) Γ β ( 1 ) λ 1 , 1 , 0 + Γ β ( 1 ) λ 0 , 1 , 0 1 2 Γ β ( 3 ) λ 0 , 3 , 0 1 2 Γ β ( 1 ) Γ γ ( 2 ) λ 0 , 1 , 2 = 0 , Γ α ( 2 ) Γ γ ( 1 ) λ 2 , 0 , 1 + 2 Γ α ( 1 ) Γ γ ( 1 ) λ 1 , 0 , 1 + Γ γ ( 1 ) λ 0 , 0 , 1 1 2 Γ β ( 2 ) Γ γ ( 1 ) λ 0 , 2 , 1 1 2 Γ γ ( 3 ) λ 0 , 0 , 3 = 0 .

When n = 2 , the system { D t i α D x j β D y k γ [ Res u 2 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 2 yields:

(3.18) Γ α ( 4 ) λ 4 , 0 , 0 + 2 Γ α ( 3 ) λ 3 , 0 , 0 + Γ α ( 2 ) λ 2 , 0 , 0 1 2 Γ α ( 2 ) Γ β ( 2 ) λ 2 , 2 , 0 1 2 Γ α ( 2 ) Γ γ ( 2 ) λ 2 , 0 , 2 = 0 , Γ α ( 3 ) Γ β ( 1 ) λ 3 , 1 , 0 + 2 Γ α ( 2 ) Γ β ( 1 ) λ 2 , 1 , 0 + Γ α ( 1 ) Γ β ( 1 ) λ 1 , 1 , 0 1 2 Γ β ( 3 ) Γ α ( 1 ) λ 1 , 3 , 0 1 2 Γ γ ( 2 ) Γ β ( 1 ) Γ α ( 1 ) λ 1 , 1 , 2 = 0 , Γ α ( 3 ) Γ γ ( 1 ) λ 3 , 0 , 1 + 2 Γ α ( 2 ) Γ γ ( 1 ) λ 2 , 0 , 1 + Γ α ( 1 ) Γ γ ( 1 ) λ 1 , 0 , 1 1 2 Γ β ( 2 ) Γ γ ( 1 ) Γ α ( 1 ) λ 1 , 2 , 1 1 2 Γ γ ( 3 ) Γ α ( 1 ) λ 1 , 0 , 3 = 0 , Γ α ( 2 ) Γ β ( 2 ) λ 2 , 2 , 0 + 2 Γ α ( 1 ) Γ β ( 2 ) λ 1 , 2 , 0 + Γ β ( 2 ) λ 0 , 2 , 0 1 2 Γ β ( 4 ) λ 0 , 4 , 0 1 2 Γ γ ( 2 ) Γ β ( 2 ) λ 0 , 2 , 2 = 0 , Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 1 ) λ 2 , 1 , 1 + 2 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 1 ) λ 1 , 1 , 1 + Γ β ( 1 ) Γ γ ( 1 ) λ 0 , 1 , 1 1 2 Γ β ( 3 ) Γ γ ( 1 ) λ 0 , 3 , 1 1 2 Γ γ ( 3 ) Γ β ( 1 ) λ 0 , 1 , 3 = 0 , Γ α ( 2 ) Γ γ ( 2 ) λ 2 , 0 , 2 + 2 Γ α ( 1 ) Γ γ ( 2 ) λ 1 , 0 , 2 + Γ γ ( 2 ) λ 0 , 0 , 2 1 2 Γ β ( 2 ) Γ γ ( 2 ) λ 0 , 2 , 2 1 2 Γ γ ( 4 ) λ 0 , 0 , 4 = 0 .

When n = 3 , the system { D t i α D x j β D y k γ [ Res u 3 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 3 yields:

(3.19) Γ α ( 5 ) λ 5 , 0 , 0 + 2 Γ α ( 4 ) λ 4 , 0 , 0 + Γ α ( 3 ) λ 3 , 0 , 0 1 2 Γ α ( 3 ) Γ β ( 2 ) λ 3 , 2 , 0 1 2 Γ α ( 3 ) Γ γ ( 2 ) λ 3 , 0 , 2 = 0 , Γ α ( 4 ) Γ β ( 1 ) λ 4 , 1 , 0 + 2 Γ α ( 3 ) Γ β ( 1 ) λ 3 , 1 , 0 + Γ α ( 2 ) Γ β ( 1 ) λ 2 , 1 , 0 1 2 Γ α ( 2 ) Γ β ( 3 ) λ 2 , 3 , 0 1 2 Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 2 ) λ 2 , 1 , 2 = 0 , Γ α ( 4 ) Γ γ ( 1 ) λ 4 , 0 , 1 + 2 Γ α ( 3 ) Γ γ ( 1 ) λ 3 , 0 , 1 + Γ α ( 2 ) Γ γ ( 1 ) λ 2 , 0 , 1 1 2 Γ α ( 2 ) Γ β ( 2 ) Γ γ ( 1 ) λ 2 , 2 , 1 1 2 Γ α ( 2 ) Γ γ ( 3 ) λ 2 , 0 , 3 = 0 , Γ α ( 3 ) Γ β ( 2 ) λ 3 , 2 , 0 + 2 Γ α ( 2 ) Γ β ( 2 ) λ 2 , 2 , 0 + Γ α ( 1 ) Γ β ( 2 ) λ 1 , 2 , 0 1 2 Γ α ( 1 ) Γ β ( 4 ) λ 1 , 4 , 0 1 2 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 2 ) λ 1 , 2 , 2 = 0 , Γ α ( 3 ) Γ β ( 1 ) Γ γ ( 1 ) λ 3 , 1 , 1 + 2 Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 1 ) λ 2 , 1 , 1 + Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 1 ) λ 1 , 1 , 1 1 2 Γ α ( 1 ) Γ β ( 3 ) Γ γ ( 1 ) λ 1 , 3 , 1 1 2 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 3 ) λ 1 , 1 , 3 = 0 , Γ α ( 3 ) Γ γ ( 2 ) λ 3 , 0 , 2 + 2 Γ α ( 2 ) Γ γ ( 2 ) λ 2 , 0 , 2 + Γ α ( 1 ) Γ γ ( 2 ) λ 1 , 0 , 2 1 2 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 2 ) λ 1 , 2 , 2 1 2 Γ α ( 1 ) Γ γ ( 4 ) λ 1 , 0 , 4 = 0 , Γ α ( 2 ) Γ β ( 3 ) λ 2 , 3 , 0 + 2 Γ α ( 1 ) Γ β ( 3 ) λ 1 , 3 , 0 + Γ β ( 3 ) λ 0 , 3 , 0 1 2 Γ β ( 5 ) λ 0 , 5 , 0 1 2 Γ β ( 3 ) Γ γ ( 2 ) λ 0 , 3 , 2 = 0 , Γ α ( 2 ) Γ β ( 2 ) Γ γ ( 1 ) λ 2 , 2 , 1 + 2 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 1 ) λ 1 , 2 , 1 + Γ β ( 2 ) Γ γ ( 1 ) λ 0 , 2 , 1 1 2 Γ β ( 4 ) Γ γ ( 1 ) λ 0 , 4 , 1 1 2 Γ γ ( 3 ) Γ β ( 2 ) λ 0 , 2 , 3 = 0 , Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 2 ) λ 2 , 1 , 2 + 2 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 2 ) λ 1 , 1 , 2 + Γ β ( 1 ) Γ γ ( 2 ) λ 0 , 1 , 2 1 2 Γ β ( 3 ) Γ γ ( 2 ) λ 0 , 3 , 2 1 2 Γ β ( 1 ) Γ γ ( 4 ) λ 0 , 1 , 4 = 0 , Γ α ( 2 ) Γ γ ( 3 ) λ 2 , 0 , 3 + 2 Γ α ( 1 ) Γ γ ( 3 ) λ 1 , 0 , 3 + Γ γ ( 3 ) λ 0 , 0 , 3 1 2 Γ β ( 2 ) Γ γ ( 3 ) λ 0 , 2 , 3 1 2 Γ γ ( 5 ) λ 0 , 0 , 5 = 0 ,

and more of the same. Solving the aforementioned sets of linear equations recursively leads to:

(3.20) λ 2 , 0 , 0 = 0 , λ 3 , 0 , 0 = 0 , λ 4 , 0 , 0 = 0 , λ 5 , 0 , 0 = 0 . λ 2 , 1 , 0 = 0 , λ 3 , 1 , 0 = 0 , λ 4 , 1 , 0 = 0 , λ 2 , 0 , 1 = 0 , λ 3 , 0 , 1 = 0 , λ 4 , 0 , 1 = 0 . λ 2 , 1 , 1 = 4 Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 1 ) , λ 3 , 1 , 1 = 8 Γ α ( 3 ) Γ β ( 1 ) Γ γ ( 1 ) , λ 2 , 2 , 0 = 0 , λ 3 , 2 , 0 = 0 , λ 2 , 0 , 2 = 0 , λ 3 , 0 , 2 = 0 . λ 2 , 2 , 1 = 0 , λ 2 , 3 , 0 = 0 , λ 2 , 0 , 3 = 0 .

We continue in this fashion until we obtain the rest of all coefficients. In general, we obtain

(3.21) λ i , 2 j + 1 , 2 k + 1 = ( 2 ) i Γ α ( i ) Γ β ( 2 j ) Γ γ ( 2 k ) , λ i , j , k = 0 , otherwise .

Compensating (3.21) in (2.1) and using Theorem 2.6, to obtain the following closed-form solution to the ( α , β , γ ) -telegraph problem:

(3.22) u ( t , x , y ) = i + j + k = 0 ( 2 ) i Γ α ( i ) Γ β ( 2 j ) Γ γ ( 2 k ) t i α x ( 2 j + 1 ) β y ( 2 k + 1 ) γ = i = 0 ( 2 t α ) i Γ α ( i ) j = 0 x ( 2 j + 1 ) β Γ β ( 2 j + 1 ) × k = 0 y ( 2 k + 1 ) γ Γ γ ( 2 k + 1 ) = E α ( 2 t α ) sinh β ( x β ) sinh γ ( y γ ) .

If α , β , γ 1 , we obtain the closed-form solution u ( t , x , y ) = e 2 t sinh ( x ) sinh ( y ) for the classical integer telegraph problem.

Figure 2 shows the cross-section behavior for the 8th-approximate series solution u 8 ( t , x , y ) of the Eq. (3.22) at various values of α , β , γ ( 0 , 1 ) . Because of the solution symmetry, the cross-sections, when γ varies, are similar to the case of β . Again, it is clear that the cross-section solutions continuously deformed into the cross-section for the integer case solution. In other words, the solution (3.22) acts like the homotopic mapping in the topological sense.

Figure 2 
               The cross-section behavior of 
                     
                        
                        
                           
                              
                                 u
                              
                              
                                 8
                              
                           
                           
                              (
                              
                                 t
                                 ,
                                 x
                                 ,
                                 y
                              
                              )
                           
                        
                        {u}_{8}\left(t,x,y)
                     
                   for (3.22) at various values of 
                     
                        
                        
                           α
                           ,
                           β
                           ,
                           γ
                           ∈
                        
                        \alpha ,\beta ,\gamma \in 
                     
                   (0,1). (a) 
                     
                        
                        
                           β
                           =
                           γ
                           =
                           1
                        
                        \beta =\gamma =1
                     
                  . (b) 
                     
                        
                        
                           α
                           =
                           γ
                           =
                           1
                        
                        \alpha =\gamma =1
                     
                  .
Figure 2

The cross-section behavior of u 8 ( t , x , y ) for (3.22) at various values of α , β , γ (0,1). (a) β = γ = 1 . (b) α = γ = 1 .

Application 3

Consider the following ( α , β , γ ) -Burgers’ problem:

(3.23) D t α [ u ( t , x , y ) ] = D x 2 β [ u ( t , x , y ) ] + D y 2 γ [ u ( t , x , y ) ] + u ( t , x , y ) D x β [ u ( t , x , y ) ] ,

with initial condition

(3.24) u ( 0 , x , y ) = x β + y γ .

We assume the existence of a solution for (3.23)–(3.24) in the form ( α , β , γ ) -FPS. To find the coefficients { λ i , j , k } i + j + k = 0 by our proposed method, we first construct the residual function for the n th-truncated series solution of Eq. (3.23):

(3.25) Res u n ( t , x , y ) = i + j + k = 0 n λ i + 1 , j , k Γ α ( i + 1 ) Γ α ( i ) t i α x j β y k γ i + j + k = 0 n λ i , j + 2 , k Γ β ( j + 2 ) Γ β ( j ) t i α x j β y k γ i + j + k = 0 n λ i , j , k + 2 Γ γ ( k + 2 ) Γ γ ( k ) t i α x j β y k γ i + j + k = 0 n λ i , j , k t i α x j β y k γ × i + j + k = 0 n λ i , j + 1 , k Γ β ( j + 1 ) Γ β ( j ) t i α x j β y k γ .

From the fractional initial condition (3.24), we have the initial coefficients for j , k 0

(3.26) λ 0 , j , k = 1 : j = 1 , k = 0 , 1 : j = 0 , k = 1 , 0 : otherwise .

Next, we solve the system of algebraic equations { D t i α D x j β D y k γ [ Res u n ( 0 , 0 , 0 ) ] = 0 } for each i + j + k = n N with taking into consideration the initial coefficients (3.26). That is, by solving the following system:

When n = 0 , the system { D t i α D x j β D y k γ [ Res u 0 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 0 yields:

(3.27) Γ α ( 1 ) λ 1 , 0 , 0 Γ β ( 2 ) λ 0 , 2 , 0 Γ γ ( 2 ) λ 0 , 0 , 2 Γ β ( 1 ) λ 0 , 0 , 0 λ 0 , 1 , 0 = 0 .

When n = 1 , the system { D t i α D x j β D y k γ [ Res u 1 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 1 yields:

(3.28) Γ α ( 2 ) λ 2 , 0 , 0 Γ α ( 1 ) Γ β ( 2 ) λ 1 , 2 , 0 Γ α ( 1 ) Γ γ ( 2 ) λ 1 , 0 , 2 Γ α ( 1 ) Γ β ( 1 ) ( λ 0 , 0 , 0 λ 1 , 1 , 0 + λ 1 , 0 , 0 λ 0 , 1 , 0 ) = 0 , Γ α ( 1 ) Γ β ( 1 ) λ 1 , 1 , 0 Γ β ( 3 ) λ 0 , 3 , 0 Γ β ( 1 ) Γ γ ( 2 ) λ 0 , 1 , 2 Γ β ( 2 ) λ 0 , 0 , 0 λ 0 , 2 , 0 + Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 0 2 = 0 , Γ α ( 1 ) Γ γ ( 1 ) λ 1 , 0 , 1 Γ β ( 2 ) Γ γ ( 1 ) λ 0 , 2 , 1 Γ γ ( 3 ) λ 0 , 0 , 3 Γ β ( 1 ) Γ γ ( 1 ) ( λ 0 , 0 , 0 λ 0 , 1 , 1 + λ 0 , 0 , 1 λ 0 , 1 , 0 ) = 0 .

When n = 2 , the system { D t i α D x j β D y k γ [ Res u 2 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 2 yields:

(3.29) Γ α ( 3 ) λ 3 , 0 , 0 Γ α ( 2 ) Γ β ( 2 ) λ 2 , 2 , 0 Γ α ( 2 ) Γ γ ( 2 ) λ 2 , 0 , 2 Γ α ( 2 ) Γ β ( 1 ) ( λ 0 , 0 , 0 λ 2 , 1 , 0 + λ 1 , 0 , 0 λ 1 , 1 , 0 + λ 0 , 1 , 0 λ 2 , 0 , 0 ) = 0 , Γ α ( 2 ) Γ β ( 1 ) λ 2 , 1 , 0 Γ α ( 1 ) Γ β ( 3 ) λ 1 , 3 , 0 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 2 ) λ 1 , 1 , 2 Γ α ( 1 ) Γ β ( 2 ) × λ 0 , 0 , 0 λ 1 , 2 , 0 + 2 Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 0 λ 1 , 1 , 0 + λ 1 , 0 , 0 λ 0 , 2 , 0 = 0 , Γ α ( 2 ) Γ γ ( 1 ) λ 2 , 0 , 1 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 1 ) λ 1 , 2 , 1 Γ α ( 1 ) Γ γ ( 3 ) λ 1 , 0 , 3 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 1 ) × ( λ 0 , 0 , 0 λ 1 , 1 , 1 + λ 0 , 1 , 1 λ 1 , 0 , 0 + λ 0 , 1 , 0 λ 1 , 0 , 1 + λ 0 , 0 , 1 λ 1 , 1 , 0 ) = 0 , Γ α ( 1 ) Γ β ( 2 ) λ 1 , 2 , 0 Γ β ( 4 ) λ 0 , 4 , 0 Γ β ( 2 ) Γ γ ( 2 ) λ 0 , 2 , 2 Γ β ( 2 ) Γ β ( 3 ) Γ β ( 2 ) λ 0 , 0 , 0 λ 0 , 3 , 0 + Γ β ( 1 ) λ 0 , 1 , 0 λ 0 , 2 , 0 + Γ β ( 2 ) Γ β ( 1 ) λ 0 , 1 , 0 λ 0 , 2 , 0 = 0 , Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 1 ) λ 1 , 1 , 1 Γ β ( 3 ) Γ γ ( 1 ) λ 0 , 3 , 1 Γ β ( 1 ) Γ γ ( 3 ) λ 0 , 1 , 3 Γ β ( 2 ) Γ γ ( 1 ) × λ 0 , 2 , 1 λ 1 , 0 , 0 + λ 0 , 2 , 0 λ 1 , 0 , 1 + 2 Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 1 λ 1 , 1 , 0 + Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 0 λ 1 , 1 , 1 + λ 0 , 0 , 1 λ 1 , 2 , 0 = 0 , Γ α ( 1 ) Γ γ ( 2 ) λ 1 , 0 , 2 Γ β ( 2 ) Γ γ ( 2 ) λ 0 , 2 , 2 Γ γ ( 4 ) λ 0 , 0 , 4 Γ β ( 1 ) Γ γ ( 2 ) ( λ 0 , 1 , 2 λ 1 , 0 , 0 + λ 0 , 1 , 1 λ 1 , 0 , 1 + λ 0 , 0 , 2 λ 1 , 1 , 0 + λ 0 , 0 , 1 λ 1 , 1 , 1 ) = 0 .

When n = 3 , the system { D t i α D x j β D y k γ [ Res u 3 ( 0 , 0 , 0 ) ] = 0 } i + j + k = 3 yields:

(3.30) Γ α ( 4 ) λ 4 , 0 , 0 Γ α ( 3 ) Γ β ( 2 ) λ 3 , 2 , 0 Γ α ( 3 ) Γ γ ( 2 ) λ 3 , 0 , 2 Γ α ( 3 ) Γ β ( 1 ) ( λ 1 , 1 , 0 λ 2 , 0 , 0 + λ 1 , 0 , 0 λ 2 , 1 , 0 + λ 0 , 1 , 0 λ 3 , 0 , 0 + λ 0 , 0 , 0 λ 3 , 1 , 0 ) = 0 , Γ α ( 3 ) Γ β ( 1 ) λ 3 , 1 , 0 Γ α ( 2 ) Γ β ( 3 ) λ 2 , 3 , 0 Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 2 ) λ 2 , 1 , 2 Γ α ( 2 ) Γ β ( 2 ) 1 Γ β ( 2 ) λ 1 , 1 , 0 2 + λ 1 , 0 , 0 λ 1 , 2 , 0 + λ 0 , 2 , 0 λ 2 , 0 , 0 + 2 Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 0 λ 2 , 1 , 0 + λ 0 , 0 , 0 λ 2 , 2 , 0 = 0 , Γ α ( 3 ) Γ γ ( 1 ) λ 3 , 0 , 1 Γ α ( 2 ) Γ β ( 2 ) Γ γ ( 1 ) λ 2 , 2 , 1 Γ α ( 2 ) Γ γ ( 3 ) λ 2 , 0 , 3 Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 1 ) ( λ 1 , 0 , 0 λ 1 , 1 , 1 + λ 0 , 1 , 1 λ 2 , 0 , 0 + λ 0 , 1 , 0 λ 2 , 0 , 1 + λ 0 , 0 , 1 λ 2 , 1 , 0 + λ 0 , 0 , 0 λ 2 , 1 , 1 + λ 1 , 0 , 1 λ 1 , 1 , 0 ) = 0 , Γ α ( 2 ) Γ β ( 2 ) λ 2 , 2 , 0 Γ α ( 1 ) Γ β ( 4 ) λ 1 , 4 , 0 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 2 ) λ 1 , 2 , 2 Γ α ( 1 ) Γ β ( 1 ) Γ β ( 3 ) Γ β ( 1 ) λ 0 , 3 , 0 λ 1 , 0 , 0 + Γ β ( 2 ) λ 0 , 2 , 0 λ 1 , 1 , 0 + Γ β 2 ( 2 ) Γ β 2 ( 1 ) λ 0 , 2 , 0 λ 1 , 1 , 0 + Γ β ( 2 ) λ 0 , 1 , 0 λ 1 , 2 , 0 + Γ β 2 ( 2 ) Γ β 2 ( 1 ) λ 0 , 1 , 0 λ 1 , 2 , 0 + Γ β ( 3 ) Γ β ( 1 ) λ 0 , 0 , 0 λ 1 , 3 , 0 = 0 , Γ α ( 2 ) Γ β ( 1 ) Γ γ ( 1 ) λ 2 , 1 , 1 Γ α ( 1 ) Γ β ( 3 ) Γ γ ( 1 ) λ 1 , 3 , 1 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 3 ) λ 1 , 1 , 3 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 1 ) × λ 2 , 0 , 1 λ 1 , 0 , 0 + λ 0 , 2 , 0 λ 1 , 0 , 1 + 2 Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 1 λ 1 , 1 , 0 + 2 Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 0 λ 1 , 1 , 1 + λ 0 , 0 , 1 λ 1 , 2 , 0 + λ 0 , 0 , 0 λ 1 , 2 , 1 = 0 , Γ α ( 2 ) Γ γ ( 2 ) λ 2 , 0 , 2 Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 2 ) λ 1 , 2 , 2 Γ α ( 1 ) Γ γ ( 4 ) λ 1 , 0 , 4 Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 2 ) ( λ 0 , 1 , 2 λ 1 , 0 , 0 + λ 0 , 1 , 1 λ 1 , 0 , 1 + λ 0 , 1 , 0 λ 1 , 0 , 2 + λ 0 , 0 , 2 λ 1 , 1 , 0 + λ 0 , 0 , 1 λ 1 , 1 , 1 + λ 0 , 0 , 0 λ 1 , 1 , 2 ) = 0 , Γ α ( 1 ) Γ β ( 3 ) λ 1 , 3 , 0 Γ β ( 5 ) λ 0 , 5 , 0 Γ β ( 3 ) Γ γ ( 2 ) λ 0 , 3 , 2 Γ β ( 3 ) Γ β ( 2 ) Γ β ( 1 ) λ 0 , 2 , 0 2 + Γ β ( 1 ) λ 0 , 1 , 0 λ 0 , 3 , 0 + Γ β ( 3 ) Γ β ( 2 ) λ 0 , 1 , 0 λ 0 , 3 , 0 + Γ β ( 4 ) Γ β ( 3 ) λ 0 , 0 , 0 λ 0 , 4 , 0 = 0 ,

Γ α ( 1 ) Γ β ( 2 ) Γ γ ( 1 ) λ 1 , 2 , 1 Γ β ( 4 ) Γ γ ( 1 ) λ 0 , 4 , 1 Γ β ( 2 ) Γ γ ( 3 ) λ 0 , 2 , 3 Γ β ( 2 ) Γ γ ( 1 ) ( Γ β ( 1 ) λ 0 , 1 , 1 λ 0 , 2 , 0 + Γ β ( 2 ) Γ β ( 1 ) λ 0 , 1 , 1 λ 0 , 2 , 0 + Γ β ( 1 ) λ 0 , 1 , 0 λ 0 , 2 , 1 + Γ β ( 2 ) Γ β ( 1 ) λ 0 , 1 , 0 λ 0 , 2 , 1 + Γ β ( 3 ) Γ β ( 2 ) λ 0 , 0 , 1 λ 0 , 3 , 0 + Γ β ( 3 ) Γ β ( 2 ) λ 0 , 0 , 0 λ 0 , 3 , 1 = 0 , Γ α ( 1 ) Γ β ( 1 ) Γ γ ( 2 ) λ 1 , 1 , 2 Γ β ( 3 ) Γ γ ( 2 ) λ 0 , 3 , 2 Γ β ( 1 ) Γ γ ( 4 ) λ 0 , 1 , 4 Γ β ( 2 ) Γ γ ( 2 ) Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 1 2 + Γ β 2 ( 1 ) Γ β ( 2 ) λ 0 , 1 , 0 λ 0 , 1 , 2 + λ 0 , 0 , 2 λ 0 , 2 , 0 + λ 0 , 0 , 1 λ 0 , 1 , 2 + λ 0 , 0 , 0 λ 0 , 2 , 2 = 0 , Γ α ( 1 ) Γ γ ( 3 ) λ 1 , 0 , 3 Γ β ( 2 ) Γ γ ( 3 ) λ 0 , 2 , 3 Γ γ ( 5 ) λ 0 , 0 , 5 Γ β ( 1 ) Γ γ ( 3 ) ( λ 0 , 0 , 3 λ 0 , 1 , 0 + λ 0 , 0 , 2 λ 0 , 1 , 1 + λ 0 , 0 , 1 λ 0 , 1 , 2 + λ 0 , 0 , 0 λ 0 , 1 , 3 ) = 0 ,

and so forth. Solving the above sets of linear equations recursively leads to:

(3.31) λ 1 , 0 , 0 = 0 , λ 2 , 0 , 0 = 0 , λ 3 , 0 , 0 = 0 , λ 1 , 1 , 0 = Γ β ( 1 ) Γ α ( 1 ) , λ 2 , 1 , 0 = 2 Γ β 2 ( 1 ) Γ α ( 2 ) , λ 3 , 1 , 0 = 4 Γ α 2 ( 1 ) Γ β 3 ( 1 ) + Γ α ( 2 ) Γ β 3 ( 1 ) Γ α 2 ( 1 ) Γ α ( 3 ) , λ 1 , 0 , 1 = Γ β ( 1 ) Γ α ( 1 ) , λ 2 , 0 , 1 = 2 Γ β 2 ( 1 ) Γ α ( 2 ) , λ 3 , 0 , 1 = 4 Γ α 2 ( 1 ) Γ β 3 ( 1 ) + Γ α ( 2 ) Γ β 3 ( 1 ) Γ α 2 ( 1 ) Γ α ( 3 ) . λ 1 , 2 , 0 = 0 , λ 2 , 2 , 0 = 0 , λ 1 , 0 , 2 = 0 , λ 2 , 0 , 2 = 0 . λ 1 , 1 , 1 = 0 .

We continue in this fashion until we obtain the rest of all coefficients. In general, we find out that the coefficients are recursively given by

(3.32) λ i , 0 , 1 = λ i , 1 , 0 , i 0 λ i , j , k = 0 , otherwise ,

where

(3.33) λ i , 1 , 0 = 1 , i = 0 Γ β ( 1 ) Γ α ( 1 ) , i = 1 Γ α ( i 1 ) Γ β ( 1 ) Γ α ( i ) k = 1 n 2 λ k 1 , 1 , 0 λ i k , 1 , 0 , i = 2 n Γ α ( i 1 ) Γ β ( 1 ) Γ α ( i ) λ n , 1 , 0 2 + k = 1 n 2 λ k 1 , 1 , 0 λ i k , 1 , 0 , i = 2 n + 1 .

Compensating (3.33) in (2.1) to obtain the following series solution to the ( α , β , γ ) -Burgers’ problem:

(3.34) u ( t , x , y ) = i + j + k = 0 λ i , j , k t i α x j β y k γ = i = 0 λ i , 1 , 0 t i α x β + i = 0 λ i , 0 , 1 t i α y γ = ( x β + y γ ) i = 0 λ i , 1 , 0 t i α .

It should be pointed out here that λ i , 1 , 0 = 1 when α , β , γ 1 . Thus, the solution for the integer Burgers’ problem is given for 0 t < 1 by

(3.35) u ( t , x , y ) = ( x + y ) i = 0 t i = x + y 1 t .

Figure 3 demonstrates again the homotopic characteristic of the solution (3.34).

Figure 3 
               The cross-section behavior of 
                     
                        
                        
                           
                              
                                 u
                              
                              
                                 10
                              
                           
                           
                              (
                              
                                 t
                                 ,
                                 x
                                 ,
                                 y
                              
                              )
                           
                        
                        {u}_{10}\left(t,x,y)
                     
                   for (3.11) at various values of 
                     
                        
                        
                           α
                           ,
                           β
                           ,
                           γ
                           ∈
                        
                        \alpha ,\beta ,\gamma \in 
                     
                   (0,1). (a) 
                     
                        
                        
                           β
                           =
                           γ
                           =
                           1
                        
                        \beta =\gamma =1
                     
                  . (b) 
                     
                        
                        
                           α
                           =
                           γ
                           =
                           1
                        
                        \alpha =\gamma =1
                     
                  .
Figure 3

The cross-section behavior of u 10 ( t , x , y ) for (3.11) at various values of α , β , γ (0,1). (a) β = γ = 1 . (b) α = γ = 1 .

4 Concluding remarks

This work intends to study the mutual impact of space-time Caputo derivatives embedded in (1 + 2)-physical models by adapting the RPSM. For this purpose, a new multivariate FPS representation that contained three Caputo derivative parameters α , β , γ ( 0 , 1 ) has been merged with the RPSM. The new adaptation has called ( α , β , γ ) -FRPSM. To boost the implementation of the proposed method, some related convergence and error results have been provided. The ( α , β , γ ) -embedding of Schrödinger, telegraph, and Burgers’ have been considered, and their solutions have been furnished as an ( α , β , γ ) -Maclaurin series that have a fractional closed-form function. We conclude with the following remarks:

  1. The proposed method has shown a great capacity to solve the ( α , β , γ ) -embedding of the physical models without requiring any restrictive assumptions or fractional and integral transformations. It has also required less computational cost than other methods derived from the celebrated Taylor’s method since it just minimizes the residual error for the truncated series solution. However, the technique assumes that the solution should exist as an analytic function in Caputo’s fractional sense.

  2. The graphical analysis of the approximate solutions has shown that the Caputo derivative parameters behave like the homotopy parameters in the topological sense to reach the integer solution case from a stationary state where the solution is the homotopy map. It also has shown that the projection of the obtained solutions into the integer space agrees significantly with the literature.

  3. The study has provided some advantageous insights to understand the function’s analyticity in a fractional sense and present the partial differential equations into a more general framework. In addition, the study has provided considerable treatment of partial differential equations that are embedded entirely in fractional space.

Finally, as future work, this idea of research can be tested on various embeddings of more physical models, expanded to approximate solutions in a bounded space, and adapted with different fractional derivative operators.

Acknowledgments

The authors would like to thank the anonymous reviewers and the editor for their valuable and constructive comments that helped improve the manuscript's quality.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: The authors declare that there are no conflicts of interest regarding the publication of this article.

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Received: 2022-03-06
Revised: 2022-07-16
Accepted: 2022-08-22
Published Online: 2022-10-10

© 2022 Mohammad Makhadmih et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  6. Improving nonlinear behavior and tensile and compressive strengths of sustainable lightweight concrete using waste glass powder, nanosilica, and recycled polypropylene fiber
  7. Two-point nonlocal nonlinear fractional boundary value problem with Caputo derivative: Analysis and numerical solution
  8. Construction of optical solitons of Radhakrishnan–Kundu–Lakshmanan equation in birefringent fibers
  9. Dynamics and simulations of discretized Caputo-conformable fractional-order Lotka–Volterra models
  10. Research on facial expression recognition based on an improved fusion algorithm
  11. N-dimensional quintic B-spline functions for solving n-dimensional partial differential equations
  12. Solution of two-dimensional fractional diffusion equation by a novel hybrid D(TQ) method
  13. Investigation of three-dimensional hybrid nanofluid flow affected by nonuniform MHD over exponential stretching/shrinking plate
  14. Solution for a rotational pendulum system by the Rach–Adomian–Meyers decomposition method
  15. Study on the technical parameters model of the functional components of cone crushers
  16. Using Krasnoselskii's theorem to investigate the Cauchy and neutral fractional q-integro-differential equation via numerical technique
  17. Smear character recognition method of side-end power meter based on PCA image enhancement
  18. Significance of adding titanium dioxide nanoparticles to an existing distilled water conveying aluminum oxide and zinc oxide nanoparticles: Scrutinization of chemical reactive ternary-hybrid nanofluid due to bioconvection on a convectively heated surface
  19. An analytical approach for Shehu transform on fractional coupled 1D, 2D and 3D Burgers’ equations
  20. Exploration of the dynamics of hyperbolic tangent fluid through a tapered asymmetric porous channel
  21. Bond behavior of recycled coarse aggregate concrete with rebar after freeze–thaw cycles: Finite element nonlinear analysis
  22. Edge detection using nonlinear structure tensor
  23. Synchronizing a synchronverter to an unbalanced power grid using sequence component decomposition
  24. Distinguishability criteria of conformable hybrid linear systems
  25. A new computational investigation to the new exact solutions of (3 + 1)-dimensional WKdV equations via two novel procedures arising in shallow water magnetohydrodynamics
  26. A passive verses active exposure of mathematical smoking model: A role for optimal and dynamical control
  27. A new analytical method to simulate the mutual impact of space-time memory indices embedded in (1 + 2)-physical models
  28. Exploration of peristaltic pumping of Casson fluid flow through a porous peripheral layer in a channel
  29. Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization
  30. Analytical analysis for non-homogeneous two-layer functionally graded material
  31. Investigation of critical load of structures using modified energy method in nonlinear-geometry solid mechanics problems
  32. Thermal and multi-boiling analysis of a rectangular porous fin: A spectral approach
  33. The path planning of collision avoidance for an unmanned ship navigating in waterways based on an artificial neural network
  34. Shear bond and compressive strength of clay stabilised with lime/cement jet grouting and deep mixing: A case of Norvik, Nynäshamn
  35. Communication
  36. Results for the heat transfer of a fin with exponential-law temperature-dependent thermal conductivity and power-law temperature-dependent heat transfer coefficients
  37. Special Issue: Recent trends and emergence of technology in nonlinear engineering and its applications - Part I
  38. Research on fault detection and identification methods of nonlinear dynamic process based on ICA
  39. Multi-objective optimization design of steel structure building energy consumption simulation based on genetic algorithm
  40. Study on modal parameter identification of engineering structures based on nonlinear characteristics
  41. On-line monitoring of steel ball stamping by mechatronics cold heading equipment based on PVDF polymer sensing material
  42. Vibration signal acquisition and computer simulation detection of mechanical equipment failure
  43. Development of a CPU-GPU heterogeneous platform based on a nonlinear parallel algorithm
  44. A GA-BP neural network for nonlinear time-series forecasting and its application in cigarette sales forecast
  45. Analysis of radiation effects of semiconductor devices based on numerical simulation Fermi–Dirac
  46. Design of motion-assisted training control system based on nonlinear mechanics
  47. Nonlinear discrete system model of tobacco supply chain information
  48. Performance degradation detection method of aeroengine fuel metering device
  49. Research on contour feature extraction method of multiple sports images based on nonlinear mechanics
  50. Design and implementation of Internet-of-Things software monitoring and early warning system based on nonlinear technology
  51. Application of nonlinear adaptive technology in GPS positioning trajectory of ship navigation
  52. Real-time control of laboratory information system based on nonlinear programming
  53. Software engineering defect detection and classification system based on artificial intelligence
  54. Vibration signal collection and analysis of mechanical equipment failure based on computer simulation detection
  55. Fractal analysis of retinal vasculature in relation with retinal diseases – an machine learning approach
  56. Application of programmable logic control in the nonlinear machine automation control using numerical control technology
  57. Application of nonlinear recursion equation in network security risk detection
  58. Study on mechanical maintenance method of ballasted track of high-speed railway based on nonlinear discrete element theory
  59. Optimal control and nonlinear numerical simulation analysis of tunnel rock deformation parameters
  60. Nonlinear reliability of urban rail transit network connectivity based on computer aided design and topology
  61. Optimization of target acquisition and sorting for object-finding multi-manipulator based on open MV vision
  62. Nonlinear numerical simulation of dynamic response of pile site and pile foundation under earthquake
  63. Research on stability of hydraulic system based on nonlinear PID control
  64. Design and simulation of vehicle vibration test based on virtual reality technology
  65. Nonlinear parameter optimization method for high-resolution monitoring of marine environment
  66. Mobile app for COVID-19 patient education – Development process using the analysis, design, development, implementation, and evaluation models
  67. Internet of Things-based smart vehicles design of bio-inspired algorithms using artificial intelligence charging system
  68. Construction vibration risk assessment of engineering projects based on nonlinear feature algorithm
  69. Application of third-order nonlinear optical materials in complex crystalline chemical reactions of borates
  70. Evaluation of LoRa nodes for long-range communication
  71. Secret information security system in computer network based on Bayesian classification and nonlinear algorithm
  72. Experimental and simulation research on the difference in motion technology levels based on nonlinear characteristics
  73. Research on computer 3D image encryption processing based on the nonlinear algorithm
  74. Outage probability for a multiuser NOMA-based network using energy harvesting relays
Heruntergeladen am 30.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/nleng-2022-0244/html
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