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A simple model of cardiac mitochondrial respiration with experimental validation

  • Received: 12 February 2021 Accepted: 06 June 2021 Published: 25 June 2021
  • Cardiac mitochondria are intracellular organelles that play an important role in energy metabolism and cellular calcium regulation. In particular, they influence the excitation-contraction cycle of the heart cell. A large number of mathematical models have been proposed to better understand the mitochondrial dynamics, but they generally show a high level of complexity, and their parameters are very hard to fit to experimental data.

    We derived a model based on historical free energy-transduction principles, and results from the literature. We proposed simple expressions that allow to reduce the number of parameters to a minimum with respect to the mitochondrial behavior of interest for us. The resulting model has thirty-two parameters, which are reduced to twenty-three after a global sensitivity analysis of its expressions based on Sobol indices.

    We calibrated our model to experimental data that consists of measurements of mitochondrial respiration rates controlled by external ADP additions. A sensitivity analysis of the respiration rates showed that only seven parameters can be identified using these observations. We calibrated them using a genetic algorithm, with five experimental data sets. At last, we used the calibration results to verify the ability of the model to accurately predict the values of a sixth dataset.

    Results show that our model is able to reproduce both respiration rates of mitochondria and transitions between those states, with very low variability of the parameters between each experiment. The same methodology may apply to recover all the parameters of the model, if corresponding experimental data were available.

    Citation: Bachar Tarraf, Emmanuel Suraniti, Camille Colin, Stéphane Arbault, Philippe Diolez, Michael Leguèbe, Yves Coudière. A simple model of cardiac mitochondrial respiration with experimental validation[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5758-5789. doi: 10.3934/mbe.2021291

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  • Cardiac mitochondria are intracellular organelles that play an important role in energy metabolism and cellular calcium regulation. In particular, they influence the excitation-contraction cycle of the heart cell. A large number of mathematical models have been proposed to better understand the mitochondrial dynamics, but they generally show a high level of complexity, and their parameters are very hard to fit to experimental data.

    We derived a model based on historical free energy-transduction principles, and results from the literature. We proposed simple expressions that allow to reduce the number of parameters to a minimum with respect to the mitochondrial behavior of interest for us. The resulting model has thirty-two parameters, which are reduced to twenty-three after a global sensitivity analysis of its expressions based on Sobol indices.

    We calibrated our model to experimental data that consists of measurements of mitochondrial respiration rates controlled by external ADP additions. A sensitivity analysis of the respiration rates showed that only seven parameters can be identified using these observations. We calibrated them using a genetic algorithm, with five experimental data sets. At last, we used the calibration results to verify the ability of the model to accurately predict the values of a sixth dataset.

    Results show that our model is able to reproduce both respiration rates of mitochondria and transitions between those states, with very low variability of the parameters between each experiment. The same methodology may apply to recover all the parameters of the model, if corresponding experimental data were available.



    Fractional calculus is a modification of the notion of differentiation and integration of arbitrary orders [1,2,3,4,5,6,7]. As there exist various prototypes in engineering and sciences, fractional calculus has attained the researcher's interest. Multiple models in numerous aspects such as; physics, biology, and engineering are tackled as per fractional differential and integral calculus. Some examples are electrochemistry, signal processing, diffusion, finance, acoustic, plasma physics, image processing, and others [8,9,10,11,12,13].

    Integral transforms are notified as one of the most suitable approaches to tackle models regarding applied mathematics, mathematical physics, engineering, and some other branches as well. The primary motivation is to deal with the provided mathematical model via any suitable integral transform and to retrieve the associated outcome in the best possible approach.

    Via an appropriate selection of integral transform, the differential and integral equations can be transformed into an algebraic equations system, which can be easily tackled.

    Various integral transforms and integral transform-based approaches are generated and incorporated for this purpose, such as; Laplace transform [14], Sumudu transform [15], Elzaki transform [16], and Natural transform [17], and many others.

    Definition 1: Shehu transform of the function θ(τ) is defined over the following functions [18,19,20].

    A={ϵ(τ):thereexistsN,k1,k2>0s.t.|θ(τ)|<Nexp(|τ|ki),whereτ(1)j×[0,)}. (1)

    Definition 2: Shehu transform of function ϵ(τ) is defined as follows [18,19,20]:

    S[ϵ(τ)]=F[s,u]=0exp[sτu]ϵ(τ)dτ. (2)

    Definition 3: Inverse Shehu transform is defined as follows [18,19,20]:

    S1[F(s,u)]=ϵ(τ). (3)

    Where s, u are the Shehu transform variables. αR.

    Definition 4: [18,19,20]

    S[ϵ(τ)]=suF[s,u]ϵ(0), (4)
    S[ϵ(τ)]=s2u2F(s,u)suϵ(0)ϵ(0), (5)
    S[ϵ(τ)]=s3u3F[s,u]s2u2ϵ(0)suϵ(0)ϵ(0). (6)

    Definition 5: Linearity property of Shehu transform [18,19,20]:

    S[c1ϵ1(τ)+c2ϵ2(τ)]=c1S[ϵ1(τ)]+c2S[ϵ2(τ)]. (7)

    Definition 6: Linearity property of inverse Shehu transform [18,19,20]:

    If

    ϵ1(τ)=S1[F1(s,u)]andϵ2(τ)=S1[F2(s,u)],

    then

    S1[c1F1(s,u)+c2F2(s,u)]=c1S1[F1(s,u)]+c2S1[F2(s,u)],
    S1[c1F1(s,u)+c2F2(s,u)]=c1ϵ1(τ)+c2ϵ2(τ).

    Definition 7: Shehu transform of Caputo fractional derivative [C.F.D.] [20,21]:

    S[Dαtϵ(μ,τ)]=sαναS[ϵ(μ,τ)]θ1r=0(sν)αr1ϵr(μ,0). (8)

    Definition 8: Mittag-Leffler function considered for two parameters was given in [22,23,24].

    Eμ,ν(n)=k=0nkΓ(kμ+ν). (9)

    Where E1,1(n)=exp(n) and E2,1(n2)=cos(n).

    In Tables 1 and 2 basic formulae regarding Shehu transform and inverse Shehu transform are provided.

    Table 1.  Chart regarding to the Shehu transform [20].
    ϵ(τ) S[ϵ(τ)]=F(s,ν)
    1. 1 νs
    2. τ ν2s2
    3. τm,mN m(νs)m+1
    4. τm,m>1 Γ(m+1)(νs)m+1
    5. eaτ νsaν
    6. sin(mτ) mν2s2+m2ν2
    7. cos(mτ) sν2s2+m2ν2
    8. sinh(mτ) mν2s2m2ν2
    9. cosh(mτ) sν2s2m2ν2

     | Show Table
    DownLoad: CSV
    Table 2.  Chart regarding to the inverse Shehu transform [20].
    F(s,ν) ϵ(τ)=S1[F(s,ν)]
    1. νs 1
    2. ν2s2 τ
    3. (νs)m+1 τmm
    4. Γ(m+1)(νs)m+1 τmΓ(m+1)
    5. νsaν eaτ
    6. mν2s2+m2ν2 sin(mτ)
    7. sν2s2+m2ν2 cos(mτ)
    8. mν2s2m2ν2 sinh(mτ)
    9. sν2s2m2ν2 cosh(mτ)

     | Show Table
    DownLoad: CSV

    1D Non-linear time-fractional Schrödinger equation.

    iDαtθ(μ,τ)=R[θ(μ,τ)]+N[θ(μ,τ)]+ϕ(μ,τ). (10)

    2D Non-linear time-fractional Schrödinger equation.

    iDαtθ(μ1,μ2,τ)=R[θ(μ1,μ2,τ)]+N[θ(μ1,μ2,τ)]+ϕ(μ1,μ2,τ). (11)

    3D Non-linear time-fractional Schrödinger equation.

    iDαtθ(μ1,μ2,μ3,τ)=R[θ(μ1,μ2,μ3,τ)]+N[θ(μ1,μ2,μ3,τ)]+ϕ(μ1,μ2,μ3,τ). (12)

    One of the well-known models in mathematical physics is Schr¨odinger equation model. There exist various implementations in numerous branches, such as; non-linear optics [25], mean-field theory of Bose-Einstein condensates [26,27], and plasma physics [28]. One emerging aspect of quantum physics is considered as fractional Schr¨odinger equation; which is associated with the notion of non-local quantum phenomena.

    Naber [29] notified time-fractional Schr¨odinger equation regarding Caputo derivative. Wang and Xu [30] elaborated on the linear Schr¨odinger equation regarding the space-fractional and time-fractional aspects as well as tackled the models via integral transform approach. Due to the existence of numerous implementations of the time-fractional Schr¨odinger equation; various researchers have worked in this field. Regarding this, novel analytical and numerical regimes have been generated for the time-fractional Schr¨odinger equation [31,32,33,34]. Hemida et al. [35] implemented HAM to provide the approximated results for the space-time fractional Schr¨odinger equation. More work related to fractional Schr¨odinger equation is provided in [36,37,38]. Other noteworthy work in this regard is notified as [39,40,41,42].

    The main advantage of ADM is that it does not rely upon perturbation or linearization or any discretization. Therefore, the actual outcome of the model remains unchanged. Discretization of variables is not demanded, which is a difficult and challenging approach. It means that the obtained results are error-free, which occurred because of discretization. Furthermore, it is accurate in finding the approximated and exact solutions of the non-linear prototypes. Such methods can be implemented to the diversified differential equations such as; integro-differential equations, differential-algebraic equations, differential-difference equations, as well as some functional equations, eigenvalue problems, and Stochastic system problems.

    The main idea of the present study is to concentrate on the implementation of Shehu ADM for attaining the exact solution to the Schr¨odinger equations in various dimensions. Some latest research regarding this field is provided on [43,44,45,46,47].

    Novelty and significance of the paper

    There are several schemes observed in the literature that deal with fractional Schr¨odinger equation in one, two, or three dimensions, but rare methods are provided that tackles fractional Schr¨odinger equation in all one, two, and three dimensions. Therefore, the authors have focused on developing a technique that proves the validity of the approximated-analytical solution of the mentioned equations in one, two, and three dimensions.

    An iterative scheme is developed in the present research regarding the solution of fractional Schr¨odinger equation in one, two, and three dimensions. The present scheme is easy to implement and needs no complex programming regarding numerical discretization. Developing the numerical programs for the fractional PDEs is not an easy task; therefore, developing such iterative schemes is the need of time to find the approximated-analytical solutions. There exist several transforms provided in the literature, but from the calculation aspect, some transforms are easy to implement, and some are not. Shehu ADM is noticed as one of the easiest methods to implement integral transform among all existing integral transforms; as in the case of Shehu ADM, no perturbation parameter is required. Via literature, it is observed that fractional Schrödinger equations have never been solved in one, two, and three dimensions with the aid of a single integral transform. Therefore, due to the importance of such equations, in this research, concentration is focused upon the solution for the same, which retains the novelty of the study. Furthermore, convergence analyses are also incorporated in the article.

    Motivation of the study

    In the present research, an iterative regime is developed and incorporated named Shehu ADM regarding the solution of fractional Schr¨odinger equation in one, two, and three dimensions. The present regime is easy to implement and needs no complex calculation in the process of numerical discretization. Generating the numerical programs to deal with the fractional PDEs is cumbersome; therefore, generating such novel iterative regimes is demanded to fetch the approximated-analytical solutions.

    There exist numerous transforms in the literature, but as per the calculation aspect, some transforms are easy to incorporate, but some are not. Shehu transform ADM is notified as one of the easiest methods. From an exploration of the literature, it is noticed that fractional Schrödinger equations are rarely solved in one, two, and three dimensions with the aid of a single integral transform-based method. Therefore, in this research, the focus is on finding the solution for the same, which contains the novelty of the research. Moreover, error analysis and convergence analysis are also elaborated in this article.

    In the present paper, the convergence of the method is checked numerically. Convergence is affirmed via Tables 38. As per Tables 38, it is observed that on increasing the number of grid points the L error norm got reduced rapidly, which is robust proof of the convergence of the generated semi-analytical techniques.

    Table 3.  Comparison of L errors at different time levels regarding Example 1.
    N L at t=1 L at t=2 L at t=3
    11 2.4979e-08 5.0711e-05 4.3236e-03
    21 4.4755e-16 4.1023e-14 2.0302e-10
    31 4.4755e-16 5.6610e-16 1.3911e-15

    Convergence up to 1016

    Convergence up to 1016

    Convergence up to 1016

     | Show Table
    DownLoad: CSV
    Table 4.  Comparison of L errors at different time levels regarding Example 2.
    N L at t=0.1 L at t=0.2 L at t=0.3
    11 7.8430e-09 1.5949e-05 1.3635e-03
    21 2.4825e-16 4.8963e-15 2.2250e-11
    31 3.7238e-16 5.5788e-16 1.1802e-15

    Convergence up to 1016

    Convergence up to 1016

    Convergence up to 1015

     | Show Table
    DownLoad: CSV
    Table 5.  Comparison of L errors at different time levels regarding Example 3.
    N L at t=1.0 L at t=1.3 L at t=1.5
    21 3.1906e-07 7.8034e-05 1.5627e-03
    31 8.4843e-15 7.0869e-12 5.9702e-10
    41 6.4393e-15 2.9407e-14 5.6576e-14

    Convergence up to 1015

    Convergence up to 1014

    Convergence up to 1014

     | Show Table
    DownLoad: CSV
    Table 6.  Comparison of L errors at different time levels regarding Example 4.
    N L at t=1.0 L at t=1.3 L at t=1.5
    11 1.8114e-06 3.2318e-05 1.5541e-04
    21 1.3092e-16 2.0510e-14 4.0767e-13
    31 2.2377e-16 2.4825e-16 2.4825e-16

    Convergence up to 1016

    Convergence up to 1016

    Convergence up to 1016

     | Show Table
    DownLoad: CSV
    Table 7.  Comparison of L errors at different time levels regarding Example 5.
    N L at t=1 L at t=2 L at t=3
    21 4.0910e-14 8.4807e-08 4.1536e-04
    31 3.3766e-16 1.9860e-15 1.5697e-10
    41 3.3766e-16 1.7342e-15 1.6577e-14

    Convergence up to 1016

    Convergence up to 1015

    Convergence up to 1014

     | Show Table
    DownLoad: CSV
    Table 8.  Comparison of L errors at different time levels regarding Example 6.
    N L at t=1 L at t=2 L at t=3
    21 4.4168e-13 9.1039e-07 4.4156e-03
    31 5.7220e-17 5.5268e-14 1.5682e-08
    41 4.6548e-17 6.9573e-16 8.8374e-15

    Convergence up to 1017

    Convergence up to 1016

    Convergence up to 1015

     | Show Table
    DownLoad: CSV

    ● The present paper is divided into different sections and subsections.

    ● In Section 3, Implementation of the Shehu ADM is developed for various kinds of fractional Schr¨odinger equations.

    ● In Sub-section 3.1, the general formula is generated for 1D time-fractional Schrödinger equation.

    ● In Sub-section 3.2, the general formula is generated for 2D time-fractional Schrödinger equation.

    ● In Sub-section 3.3, the general formula is generated for 3D time-fractional Schrödinger equation.

    ● In Section 4, six examples are elaborated to validate the efficiency and efficacy of the developed regime.

    ● In Section 5, graphical analysis, error analysis, and convergence analysis is notified.

    ● Section 6 is provided as the concluding remarks.

    Applying Shehu transform upon Eq (1):

    iS[Dατθ(μ,τ)]=S[R[θ(μ,τ)]+N[θ(μ,τ)]+ϕ(μ,τ)]S[Dατθ(μ,τ)]=iS[R[θ(μ,τ)]+N[θ(μ,τ)]+ϕ(μ,τ)](sν)αS[θ(μ,τ)]θ1r=0(sν)αr1θr(μ,0)=iS[R[θ(μ,τ)]+N[θ(μ,τ)]+ϕ(μ,τ)](sν)αS[θ(μ,τ)]=θ1r=0(sν)αr1θr(μ,0)iS[R[θ(μ,τ)]+N[θ(μ,τ)]+ϕ(μ,τ)]S[θ(μ,τ)]=(νs)αθ1r=0(sν)αr1θr(μ,0)i(νs)αS[R[θ(μ,τ)]+N[θ(μ,τ)]+ϕ(μ,τ)]θ(μ,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ,0)]iS1[(νs)αS[R[θ(μ,τ)]+N[θ(μ,τ)]+ϕ(μ,τ)]]n=0θn(μ,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ,0)+ϕ(μ,τ)]iS1[(νs)αS[R[n=0θn(μ,τ)]+N[n=0θn(μ,τ)]]]. (13)

    Where,

    θ0(μ,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ,0)+ϕ(μ,τ)]. (14)
    θn+1(μ,τ)=iS1[(νs)αS[R[n=0θn(μ,τ)]+N[n=0θn(μ,τ)]]],n=0,1,2,3, (15)

    Applying Shehu transform upon Eq (2):

    iS[Dαtθ(μ1,μ2,τ)]=S[R[θ(μ1,μ2,τ)]+N[θ(μ1,μ2,τ)]+ϕ(μ1,μ2,τ)]S[Dαtθ(μ1,μ2,τ)]=iS[R[θ(μ1,μ2,τ)]+N[θ(μ1,μ2,τ)]+ϕ(μ1,μ2,τ)](sν)αS[θ(μ1,μ2,τ)]θ1r=0(sν)αr1θr(μ1,μ2,0)=iS[R[θ(μ1,μ2,τ)]+N[θ(μ1,μ2,τ)]+ϕ(μ1,μ2,τ)](sν)αS[θ(μ1,μ2,τ)]=θ1r=0(sν)αr1θr(μ1,μ2,0)iS[R[θ(μ1,μ2,τ)]+N[θ(μ1,μ2,τ)]+ϕ(μ1,μ2,τ)]S[θ(μ1,μ2,τ)]=(νs)αθ1r=0(sν)αr1θr(μ1,μ2,0)i(νs)αS[R[θ(μ1,μ2,τ)]+N[θ(μ1,μ2,τ)]+ϕ(μ1,μ2,τ)]θ(μ1,μ2,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ1,μ2,0)]iS1[(νs)αS[R[θ(μ1,μ2,τ)]+N[θ(μ1,μ2,τ)]+ϕ(μ1,μ2,τ)]]n=0θn(μ1,μ2,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ1,μ2,0)+ϕ(μ1,μ2,τ)]iS1[(νs)αS[R[n=0θn(μ1,μ2,τ)]+N[n=0θn(μ1,μ2,τ)]]]. (16)

    Where,

    θ0(μ1,μ2,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ1,μ2,0)+ϕ(μ1,μ2,τ)].θn+1(μ1,μ2,τ) (17)
    =iS1[(νs)αS[R[n=0θn(μ1,μ2,τ)]+N[n=0θn(μ1,μ2,τ)]]],n=0,1,2,3, (18)

    Applying Shehu transform upon Eq (3):

    iS[Dαtθ(μ1,μ2,μ3,τ)]=S[R[θ(μ1,μ2,μ3,τ)]+N[θ(μ1,μ2,μ3,τ)]+ϕ(x,y,z,t)]S[Dαtθ(μ1,μ2,μ3,τ)]=iS[R[θ(μ1,μ2,μ3,τ)]+N[θ(μ1,μ2,μ3,τ)]+ϕ(x,y,z,t)](sν)αS[θ(μ1,μ2,μ3,τ)]θ1r=0(sν)αr1θr(μ1,μ2,μ3,0)=iS[R[θ(μ1,μ2,μ3,τ)]+N[θ(μ1,μ2,μ3,τ)]+ϕ(μ1,μ2,μ3,τ)](sν)αS[θ(μ1,μ2,μ3,τ)]=θ1r=0(sν)αr1θr(μ1,μ2,μ3,0)iS[R[θ(μ1,μ2,μ3,τ)]+N[θ(μ1,μ2,μ3,τ)]+ϕ(μ1,μ2,μ3,τ)]S[θ(μ1,μ2,μ3,τ)]=(νs)αθ1r=0(sν)αr1θr(μ1,μ2,μ3,0)i(νs)αS[R[θ(μ1,μ2,μ3,τ)]+N[θ(μ1,μ2,μ3,τ)]+ϕ(μ1,μ2,μ3,τ)]θ(μ1,μ2,μ3,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ1,μ2,μ3,0)]iS1[(νs)αS[R[θ(μ1,μ2,μ3,τ)]+N[θ(μ1,μ2,μ3,τ)]+ϕ(μ1,μ2,μ3,τ)]]n=0θn(μ1,μ2,μ3,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ1,μ2,μ3,0)+ϕ(μ1,μ2,μ3,τ)]iS1[(νs)αS[R[n=0θn(μ1,μ2,μ3,τ)]+N[n=0θn(μ1,μ2,μ3,τ)]]]. (19)

    Where,

    θ0(μ1,μ2,μ3,τ)=S1[(νs)αθ1r=0(sν)αr1θr(μ1,μ2,μ3,0)+ϕ(μ1,μ2,μ3,τ)].θn+1(μ1,μ2,μ3,τ) (20)
    =iS1[(νs)αS[R[n=0θn(μ1,μ2,μ3,τ)]+N[n=0θn(μ1,μ2,μ3,τ)]]],n=0,1,2,3, (21)

    In the present section, six examples are tested to ensure the validity of the proposed regime, Examples 1–4 are associated with 1D time-fractional Schr¨odinger. Example 5 is provided regarding 2D time-fractional Schrödinger. Example 6 is associated with 3D time-fractional Schrödinger equation. In all the provided cases, approximated and exact profiles are generated.

    Example 1: Considered 1D non-linear time-fractional Schr¨odinger as follows [36]:

    iDαtθ+θμμ+2|θ|2θ=0. (22)

    I.C.: θ(μ,0)=eiμ.

    Applying Shehu transform upon Eq (22):

    iS[Dατθ(μ,τ)]=S[θμμ(μ,τ)+2|θ(μ,τ)|2θ(μ,τ)]S[Dατθ(μ,τ)]=iS[θμμ(μ,τ)+2|θ(μ,τ)|2θ(μ,τ)](sν)αS[θ(μ,τ)]ξ1r=0(sν)αr1θr(μ,0)=iS[θμμ(μ,τ)+2|θ(μ,τ)|2θ(μ,τ)](sν)αS[θ(μ,τ)]=ξ1r=0(sν)αr1θr(μ,0)+iS[θμμ(μ,τ)+2|θ(μ,τ)|2θ(μ,τ)]S[θ(μ,τ)]=(νs)αξ1r=0(sν)αr1θr(μ,0)+i(νs)αS[θμμ(μ,τ)+2|θ(μ,τ)|2θ(μ,τ)]θ(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]+iS1[(νs)αS[θμμ(μ,τ)+2|θ(μ,τ)|2θ(μ,τ)]]n=0θn(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]+iS1[(νs)αS[(n=0θn(μ,τ))μμ+2n=0An]]. (23)

    Where

    F(θ)=|θ(μ,τ)|2θ(μ,τ)=n=0An.
    θ0(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)].
    θn+1(μ,τ)=iS1[(νs)αS[(θn(μ,τ))μμ+2An]],n=0,1,2,3,

    Considered, ξ=1:

    θ0(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]
    θ0(μ,τ)=S1[(νs)α(sν)α1θ(μ,0)]
    θ0(μ,τ)=θ(μ,0)=eiμ.

    Considered n=0:

    θ1(μ,τ)=iS1[(νs)αS[(θ0(μ,τ))μμ+2A0]].

    Where,

    (θ0)μμ(μ,τ)=eix,A0=F(θ0)=θ20θ0=eiμ.
    θ1(μ,τ)=iS1[(νs)αS[eiμ+2eiμ]].
    θ1(μ,τ)=iS1[(νs)αS[eiμ]]
    θ1(μ,τ)=ieiμS1[(νs)αS[1]]
    θ1(μ,τ)=ieiμS1[(νs)2α]θ1(μ,τ)=ieiμt2α1Γ(2α).

    Considered n=1:

    θ2(μ,τ)=iS1[(νs)αS[(θ1)μμ+2A1]].

    Where,

    (θ1)μμ(μ,τ)=ieiμτ2α1Γ(2α),
    A1=2θ0θ1θ0+θ20θ1=ieiμτ2α1Γ(2α),
    θ2(μ,τ)=iS1[(νs)αS[ieiμτ2α1Γ(2α)+2ieiμτ2α1Γ(2α)]]
    θ2(μ,τ)=iS1[(νs)αS[ieiμτ2α1Γ(2α)]]
    θ2(μ,τ)=(i2eiμ)S1[(νs)α(νs)2α]
    θ2(μ,τ)=(i2eiμ)S1[(νs)3α]
    θ2(μ,τ)=i2eiμτ3α1Γ(3α),
    θ(μ,τ)=θ0(μ,τ)+θ1(μ,τ)+θ2(μ,τ)+θ3(μ,τ)+
    θ(μ,τ)=eiμ+ieiμτ2α1Γ(2α)+i2eiμτ3α1Γ(3α)+
    θ(μ,τ)=eiμ[1+iτ2α1Γ(2α)+i2τ3α1Γ(3α)+].

    Considered α=1:

    θ(μ,τ)=eiμ[1+iτ1!+(iτ)22!+]
    θ(μ,τ)=ei[μ+t].

    Example 2: Considered 1D linear time-fractional Schr¨odinger equation as follows [37]:

    Dατθ(μ,τ)+iθμμ(μ,τ)=0. (23)

    I.C.: θ(μ,0)=e3iμ

    Applying Shehu transform in Eq (23):

    (sν)αS[θ(μ,τ)]ξ1r=0(sν)αr1θr(μ,0)=iS[θμμ(μ,τ)]
    (sν)αS[θ(μ,τ)]=ξ1r=0(sν)αr1θr(μ,0)iS[θμμ(μ,τ)]
    S[θ(μ,τ)]=(νs)αξ1r=0(sν)αr1θr(μ,0)i(νs)αS[θμμ(μ,τ)]
    θ(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]iS1[(νs)αS[θμμ(μ,τ)]]
    n=0θn(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]iS1[(νs)αS[(n=0θn(μ,τ))μμ]],
    θ0(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)],
    θn+1(μ,τ)=iS1[(νs)αS[(θn(μ,τ))μμ]],n=0,1,2,3,

    Considered ξ=1:

    θ0(μ,τ)=S1[(νs)α(sν)α1θ(μ,0)]
    θ0(μ,τ)=S1[(νs)θ(μ,0)]
    θ0(μ,τ)=θ(μ,0)S1[νs]
    θ0(μ,τ)=θ(μ,0)=e3iμ.

    Considered n=0:

    θ1(μ,τ)=iS1[(νs)αS[(θ0(μ,τ))μμ]],(θ0)μμ=(3i)2e3iμ
    θ1(μ,τ)=iS1[(νs)αS[(3i)2e3iμ]]
    θ1(μ,τ)=i(3i)2e3iμS1[(νs)αS[1]]
    θ1(μ,τ)=i(3i)2e3iμS1[(νs)2α]
    θ1(μ,τ)=9ie3iμτ2α1Γ(2α).

    Considered n=1:

    θ2(μ,τ)=iS1[(νs)αS[(θ1)μμ]],(θ1(μ,τ))μμ=81i3e3iμτ2α1Γ(2α)
    θ2(μ,τ)=iS1[(νs)αS[81i3e3iμτ2α1Γ(2α)]]
    θ2(μ,τ)=i(81i3e3iμ)S1[(νs)αS[τ2α1Γ(2α)]]
    θ2(μ,τ)=i(81i3e3iμ)S1[(νs)3α]
    θ2(μ,τ)=81i2e3iμτ3α1Γ(3α)
    θ(μ,τ)=θ0(μ,τ)+θ1(μ,τ)+θ2(μ,τ)+θ3(μ,τ)+
    θ(μ,τ)=e3iμ+9ie3iμτ2α1Γ(2α)+81i2e3iμτ3α1Γ(3α)+

    Considered α=1:

    θ(μ,τ)=e3iμ[1+9iτ1!+(9iτ)22!+]
    θ(μ,τ)=e3i[μ+3τ].

    Example 3: Considered 1D linear time-fractional Schr¨odinger equation as follows [37]:

    Dατθ(μ,τ)+iθμμ(μ,τ)=0. (24)

    I.C.: θ(μ,0)=1+cosh(2μ)

    Applying Shehu transform upon Eq (24):

    S[Dατθ]=iS[θμμ]
    (sν)αS[θ(μ,τ)]ξ1r=0(sν)αr1θr(μ,0)=iS[θμμ(μ,τ)]
    (sν)αS[θ(μ,τ)]=ξ1r=0(sν)αr1θr(μ,0)iS[θμμ(μ,τ)]
    S[θ(μ,τ)]=(νs)αξ1r=0(sν)αr1θr(μ,0)i(νs)αS[θμμ(μ,τ)]
    θ(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]iS1[(νs)αS[θμμ(μ,τ)]]
    n=0θn(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]iS1[(νs)αS[(n=0θn(μ,τ))μμ]].

    Where

    θ0(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)].
    θn+1(μ,τ)=iS1[(νs)αS[(θn(μ,τ))μμ]],n=0,1,2,3,

    Considered ξ=1:

    θ0(μ,τ)=S1[(νs)α(sν)α1θ(μ,0)]
    θ0(μ,τ)=θ(x,0)S1[νs]
    θ0(μ,τ)=θ(μ,0)=1+cosh(2μ).

    Considered n=0:

    θ1(μ,τ)=iS1[(νs)αS[(θ0(μ,τ))μμ]],(θ0)μμ=4cosh(2μ)
    θ1(μ,τ)=iS1[(νs)αS[4cosh(2μ)]]
    θ1(μ,τ)=4icosh(2μ)S1[(νs)2α]
    θ1(μ,τ)=4icosh(2μ)τ2α1Γ(2α).

    Considered n=1:

    θ2(μ,τ)=iS1[(νs)αS[(θ1)μμ]],(θ1(μ,τ))μμ=16icosh(2μ)τ2α1Γ(2α)
    θ2(μ,τ)=iS1[(νs)αS[16icosh(2μ)τ2α1Γ(2α)]]
    θ2(μ,τ)=16i2cosh(2μ)S1[(νs)3α]
    θ2(μ,τ)=16i2cosh(2μ)τ3α1Γ(3α)
    θ(μ,τ)=θ0(μ,τ)+θ1(μ,τ)+θ2(μ,τ)+θ3(μ,τ)+
    θ(μ,τ)=1+cosh(2μ)4icosh(2μ)τ2α1Γ(2α)+16i2cosh(2μ)τ3α1Γ(3α)

    Considered α=1:

    θ(μ,τ)=1+cosh(2μ)[14iτ1!+(4iτ)22!]
    θ(μ,τ)=1+cosh(2μ)exp[4iτ].

    Example 4: Considered 1D non-linear time-fractional Schr¨odinger equation as follows [38]:

    iDαtθ(μ,τ)+12θμμ(μ,τ)θ(μ,τ)cos2μθ(μ,τ)|θ(μ,τ)|2=0,μ[0,1]. (25)

    I.C.: u(μ,0)=sinμ

    Applying Shehu transform upon Eq (25):

    iS[Dατθ(μ,τ)]=S[12uμμ(μ,τ)+θ(μ,τ)cos2μ+θ(μ,τ)|θ(μ,τ)|2]
    S[Dατθ(μ,τ)]=iS[12uμμ(μ,τ)+θ(μ,τ)cos2μ+θ(μ,τ)|θ(μ,τ)|2]
    (sν)αS[θ(μ,τ)]ξ1r=0(sν)αr1θr(μ,0)=iS[12uμμ(μ,τ)+θ(μ,τ)cos2μ+θ(μ,τ)|θ(μ,τ)|2]
    (sν)αS[θ(μ,τ)]=ξ1r=0(sν)αr1θr(μ,0)iS[12uμμ(μ,τ)+θ(μ,τ)cos2μ+θ(μ,τ)|θ(μ,τ)|2]
    S[θ(μ,τ)]=(νs)αξ1r=0(sν)αr1θr(μ,0)i(νs)αS[12uμμ(μ,τ)+θ(μ,τ)cos2μ+θ(μ,τ)|θ(μ,τ)|2]
    θ(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]iS1[(νs)αS[12uμμ(μ,τ)+θ(μ,τ)cos2μ+θ(μ,τ)|θ(μ,τ)|2]]
    n=0θn(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)]+iS1[(νs)αS[12(n=0un(μ,τ))μμcos2μ(n=0un(μ,τ))n=0An]]
    θ0(μ,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ,0)],
    θn+1(μ,τ)=iS1[(νs)αS[12(θn(μ,τ))μμcos2μ(θn(μ,τ))An]],n=0,1,2,3,

    Considered ξ=1:

    θ0(μ,τ)=S1[(νs)α(sν)α1θ(μ,0)]
    θ0(μ,τ)=θ(μ,0)S1[νs]
    θ0=u(μ,0)=sinμ.

    Considered n=0:

    θ1(μ,τ)=iS1[(νs)αS[12(θ0)μμcos2μ(θ0)A0]].

    Where,

    (θ0)μμ=sinμ,A0=F(θ0)=θ20θ0=sin3μ
    θ1(μ,τ)=iS1[(νs)αS[12(sinμ)cos2x(sinμ)sin3μ]]
    θ1(μ,τ)=iS1[(νs)αS[32sinμ]]
    θ1(μ,τ)=3i2sinμS1[(νs)2α]
    θ1(μ,τ)=3i2sinμτ2α1Γ(2α).

    Considered n=1:

    θ2(μ,τ)=iS1[(νs)αS[12(θ1(μ,τ))μμcos2μ(θ1(μ,τ))A1]].

    Where

    (θ1)μμ(μ,τ)=(3i2)sinμτ2α1Γ(2α),A1=2θ0θ1θ0+θ20θ1=(3i2)sin3μτ2α1Γ(2α)
    θ2(μ,τ)=iS1[(νs)αS[12((3i2)sinμτ2α1Γ(2α))cos2μ(3i2sinμτ2α1Γ(2α))((3i2)sin3μτ2α1Γ(2α))]]
    θ2(μ,τ)=(9i24)sinμS1[(νs)αS[τ2α1Γ(2α)]]
    θ2(μ,τ)=(9i24)sinμS1[(νs)3α]
    θ2(μ,τ)=(9i24)sinμτ3α1Γ(3α)
    θ(μ,τ)=θ0(μ,τ)+θ1(μ,τ)+θ2(μ,τ)+θ3(μ,τ)+
    θ(μ,τ)=sinμ3i2sinμτ2α1Γ(2α)+(9i24)sinμτ3α1Γ(3α)

    Considered α=1:

    θ(μ,τ)=sinμ[1(3iτ2)1!+(3iτ2)22!]
    θ(μ,τ)=sinμexp[3iτ2].

    Example 5: Considered 2D non-linear time-fractional Schr¨odinger equation as follows [38]:

    iDατθ=12[θμ1μ1+θμ2μ2]+(1sin2μ1sin2μ2)θ+θ|θ|2, (26)

    where μ1,μ­2[0,2π]×[0,2π].

    I.C.: θ(μ1,μ2,0)=sinμ1sinμ2

    Applying Shehu transform in Eq (26):

    iS[Dατθ(μ1,μ2,τ)]=S[12[θμ1μ1(μ1,μ2,τ)+θμ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θ(μ1,μ2,τ)+θ(μ1,μ2,τ)|θ(μ1,μ2,τ)|2]
    S[Dατθ(μ1,μ2,τ)]=iS[12[θμ1μ1(μ1,μ2,τ)+θμ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θ(μ1,μ2,τ)+θ(μ1,μ2,τ)|θ(μ1,μ2,τ)|2]
    (sν)αS[θ(μ1,μ2,τ)]ξ1r=0(sν)αr1θr(μ1,μ2,0)=iS[12[θμ1μ1(μ1,μ2,τ)+θμ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θ(μ1,μ2,τ)+θ(μ1,μ2,τ)|θ(μ1,μ2,τ)|2]
    (sν)αS[θ(μ1,μ2,τ)]=ξ1r=0(sν)αr1θr(μ1,μ2,0)iS[12[θμ1μ1(μ1,μ2,τ)+θμ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θ(μ1,μ2,τ)+θ(μ1,μ2,τ)|θ(μ1,μ2,τ)|2]
    S[θ(μ1,μ2,τ)]=(νs)αξ1r=0(sν)αr1θr(μ1,μ2,0)i(νs)αS[12[θμ1μ1(μ1,μ2,τ)+θμ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θ(μ1,μ2,τ)+θ(μ1,μ2,τ)|θ(μ1,μ2,τ)|2]
    θ(μ1,μ2,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ1,μ2,0)]iS1[(νs)αS[12[θμ1μ1(μ1,μ2,τ)+θμ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θ(μ1,μ2,τ)+θ(μ1,μ2,τ)|θ(μ1,μ2,τ)|2]]
    n=0θn(μ1,μ2,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ1,μ2,0)]iS1[(νs)αS[12[(n=0θn(μ1,μ2,τ))μ1μ1+(n=0θn(μ1,μ2,τ))μ2μ2]+(1sin2μ1sin2μ2)n=0un(μ1,μ2,τ)+n=0An]]
    u0(μ1,μ2,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ1,μ2,0)],
    un+1(μ1,μ2,τ)=iS1[(νs)αS[12[(θn)μ1μ1(μ1,μ2,τ)+(θn)μ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θn(μ1,μ2,τ)+An]],n=0,1,2,3,

    Considered ξ=1:

    θ0(μ1,μ2,τ)=S1[(νs)α(sν)α1θ(μ1,μ2,0)]
    θ0(μ1,μ2,τ)=θ(μ1,μ2,0),
    θ0(μ1,μ2,τ)=sinμ1sinμ2.

    Considered n=0:

    u1(μ1,μ2,τ)=iS1[(νs)αS[12[(θ0)μ1μ1(μ1,μ2,τ)+(θ0)μ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)θ0(μ1,μ2,τ)+A0]].

    Where

    (θ0)μ1μ1(μ1,μ2,τ)=(θ0)μ2μ2(μ1,μ2,τ)=sinμ1sinμ2,

    and

    A0=θ20θ0=sin3μ1sin3μ2
    θ1(μ1,μ2,τ)
    =iS1[(νs)αS[12[2sinμ1sinμ2]+(1sin2μ1sin2μ2)(sinμ1sinμ2)+sin3μ1sin3μ2]]
    θ1(μ1,μ2,τ)=iS1[(νs)αS[2sinμ1sinμ2]]
    θ1(μ1,μ2,τ)=i(2sinμ1sinμ2)S1[(νs)αS[1]]
    θ1(μ1,μ2,τ)=2isinμ1sinμ2S1[(νs)2α]
    θ1(μ1,μ2,τ)=2isinμ1sinμ2τ2α1Γ(2α).

    Considered n=1:

    θ2(μ1,μ2,τ)=iS1[(νs)αS[12[(u1)μ1μ1(μ1,μ2,τ)+(u1)μ2μ2(μ1,μ2,τ)]+(1sin2μ1sin2μ2)u1(μ1,μ2,τ)+A1]].

    Where,

    (θ1)μ1μ1=(θ1)μ2μ2=2isinμ1sinμ2τ2α1Γ(2α),
    (θ1)μ1μ1+(θ1)μ2μ2=4isinμ1sinμ2τ2α1Γ(2α),
    A1=2θ0θ1θ0+θ20θ1=2isin3μ1sin3μ2τ2α1Γ(2α)
    θ2(μ1,μ2,τ)
    =iS1[(νs)αS[12[4isinμ1sinμ2τ2α1Γ(2α)]+(1sin2μ1sin2μ2)(2isinμ1sinμ2τ2α1Γ(2α))+(2isin3xsin3yτ2α1Γ(2α))]]
    θ2(μ1,μ2,τ)=iS1[(νs)αS{4isinμ1sinμ2τ2α1Γ(2α)}]
    θ2(μ1,μ2,τ)=4i2sinμ1sinμ2S1[(νs)3α]
    θ2(μ1,μ2,τ)=4i2sinμ1sinμ2τ3α1Γ(3α)
    θ(μ1,μ2,τ)=u0(μ1,μ2,τ)+u1(μ1,μ2,τ)+u2(μ1,μ2,τ)+u3(μ1,μ2,τ)+
    θ(μ1,μ2,τ)=sinμ1sinμ22isinμ1sinμ2τ2α1Γ(2α)+4i2sinμ1sinμ2τ3α1Γ(3α)

    Considered α=1:

    θ(μ1,μ2,τ)=sinμ1sinμ2[12iτ1!+(2iτ)22!]
    θ(μ1,μ2,τ)=sinμ1sinμ2exp(2iτ).

    Example 6: Considered 2D non-linear time-fractional Schr¨odinger equation as follows [34]:

    iDατθ(μ1,μ2,μ3,τ)=12[θμ1μ1(μ1,μ2,μ3,τ)+θμ2μ2(μ1,μ2,μ3,τ)+θμ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ(μ1,μ2,μ3,τ)+θ(μ1,μ2,μ3,τ)|θ(μ1,μ2,μ3,τ)|2. (27)

    Where μ1,μ2,μ3[0,2π]×[0,2π]×[0,2π].

    I.C.: θ(μ1,μ2,μ3,0)=sinμ1sinμ2sinμ3

    Applying Shehu transform in Eq (27):

    iS[Dαtθ(μ1,μ2,μ3,τ)]=S[12[θμ1μ1(μ1,μ2,μ3,τ)+θμ2μ2(μ1,μ2,μ3,τ)+θμ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ(μ1,μ2,μ3,τ)+θ(μ1,μ2,μ3,τ)|θ(μ1,μ2,μ3,τ)|2]
    S[Dαtθ(μ1,μ2,μ3,τ)]=iS[12[θμ1μ1(μ1,μ2,μ3,τ)+θμ2μ2(μ1,μ2,μ3,τ)+θμ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ(μ1,μ2,μ3,τ)+θ(μ1,μ2,μ3,τ)|θ(μ1,μ2,μ3,τ)|2]
    (sν)αS[θ(μ1,μ2,μ3,τ)]ξ1r=0(sν)αr1ur(x,y,z,0)=iS[12[θμ1μ1(μ1,μ2,μ3,τ)+θμ2μ2(μ1,μ2,μ3,τ)+θμ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ(μ1,μ2,μ3,τ)+θ(μ1,μ2,μ3,τ)|θ(μ1,μ2,μ3,τ)|2]
    (sν)αS[θ(μ1,μ2,μ3,τ)]=ξ1r=0(sν)αr1θr(x,y,z,0)iS[12[θμ1μ1(μ1,μ2,μ3,τ)+θμ2μ2(μ1,μ2,μ3,τ)+θμ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ(μ1,μ2,μ3,τ)+θ(μ1,μ2,μ3,τ)|θ(μ1,μ2,μ3,τ)|2]
    S[θ(μ1,μ2,μ3,τ)]=(νs)αξ1r=0(sν)αr1θr(μ1,μ2,μ3,0)i(νs)αS[12[θμ1μ1(μ1,μ2,μ3,τ)+θμ2μ2(μ1,μ2,μ3,τ)+θμ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ(μ1,μ2,μ3,τ)+θ(μ1,μ2,μ3,τ)|θ(μ1,μ2,μ3,τ)|2]
    θ(μ1,μ2,μ3,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ1,μ2,μ3,0)]iS1[(νs)αS[12[θμ1μ1(μ1,μ2,μ3,τ)+θμ2μ2(μ1,μ2,μ3,τ)+θμ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ(μ1,μ2,μ3,τ)+θ(μ1,μ2,μ3,τ)|θ(μ1,μ2,μ3,τ)|2]]
    n=0θn(μ1,μ2,μ3,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ1,μ2,μ3,0)]iS1[(νs)αS[12[(n=0θn(μ1,μ2,μ3,τ))μ1μ1+(n=0θn(μ1,μ2,μ3,τ))μ2μ2+(n=0θn(μ1,μ2,μ3,τ))μ3μ3]+(1sin2μ1sin2μ2sin2μ3)n=0θn(μ1,μ2,μ3,τ)+n=0An]],
    θ0(μ1,μ2,μ3,τ)=S1[(νs)αξ1r=0(sν)αr1θr(μ1,μ2,μ3,0)],
    θn+1(μ1,μ2,μ3,τ)=iS1[(νs)αS[12[(θn)μ1μ1(μ1,μ2,μ3,τ)+(θn)μ2μ2(μ1,μ2,μ3,τ)+(θn)μ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θn(μ1,μ2,μ3,τ)+An]],n=0,1,2,3,

    Considered ξ=1:

    θ0(μ1,μ2,μ3,τ)=S1[(νs)α(sν)α1θ(μ1,μ2,μ3,0)]
    θ0(μ1,μ2,μ3,τ)=θ(μ1,μ2,μ3,0)
    θ0(μ1,μ2,μ3,τ)=sinμ1sinμ2sinμ3.

    Considered n=0:

    θ1(μ1,μ2,μ3,τ)=iS1[(νs)αS[12[(θ0)μ1μ1(μ1,μ2,μ3,τ)+(θ0)μ2μ2(μ1,μ2,μ3,τ)+(θ0)μ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ0(μ1,μ2,μ3,τ)+A0]].

    Where

    (θ0)μ1μ1(μ1,μ2,μ3,τ)=(θ0)μ2μ2(μ1,μ2,μ3,τ)=(θ0)μ3μ3(μ1,μ2,μ3,τ)=sinμ1sinμ2sinμ3(θ0)μ1μ1(μ1,μ2,μ3,τ)+(θ0)μ2μ2(μ1,μ2,μ3,τ)+(θ0)μ3μ3(μ1,μ2,μ3,τ)=3sinμ1sinμ2sinμ3,

    and

    A0=θ20θ0=sin3μ1sin3μ2sin3μ3
    θ1(μ1,μ2,μ3,τ)
    =iS1[(νs)αS[12[3sinμ1sinμ2sinμ3]+(1sin2μ1sin2μ2sin2μ3)(sinμ1sinμ2sinμ3)+sin3μ1sin3μ2sin3μ3]]
    θ1(μ1,μ2,μ3,τ)=iS1[(νs)αS[52sinμ1sinμ2sinμ3]]
    θ1(μ1,μ2,μ3,τ)=5i2sinμ1sinμ2sinμ3S1[(νs)αS[1]]
    θ1(μ1,μ2,μ3,τ)=5i2sinμ1sinμ2sinμ3S1[(νs)2α]
    θ1(μ1,μ2,μ3,τ)=5i2sinμ1sinμ2sinμ3τ2α1Γ(2α).

    Considered n=1:

    θ2(μ1,μ2,μ3,τ)=iS1[(νs)αS[12[(θ1)μ1μ1(μ1,μ2,μ3,τ)+(θ1)μ2μ2(μ1,μ2,μ3,τ)+(θ1)μ3μ3(μ1,μ2,μ3,τ)]+(1sin2μ1sin2μ2sin2μ3)θ1(μ1,μ2,μ3,τ)+A1]].

    Where,

    (θ1)μ1μ1(μ1,μ2,μ3,τ)=(θ1)μ2μ2(μ1,μ2,μ3,τ)=(θ1)μ3μ3(μ1,μ2,μ3,τ)
    =(5i2)sinμ1sinμ2sinμ3τ2α1Γ(2α).
    A1=2θ0θ1θ0+θ20θ1=5i2μ1sin3μ2sin3μ3τ2α1Γ(2α)
    θ2(μ1,μ2,μ3,τ)=iS1[(νs)αS{25i4sinμ1sinμ2sinμ3τ2α1Γ(2α)}]
    θ2(μ1,μ2,μ3,τ)=25i24sinμ1sinμ2sinμ3S1[(νs)3α]
    θ2(μ1,μ2,μ3,τ)=25i24sinμ1sinμ2sinμ3τ3α1Γ(3α)
    θ(μ1,μ2,μ3,τ)=θ0(μ1,μ2,μ3,τ)+θ1(μ1,μ2,μ3,τ)+θ2(μ1,μ2,μ3,τ)+θ3(μ1,μ2,μ3,τ)+
    θ(μ1,μ2,μ3,τ)=sinμ1sinμ2sinμ35i2sinμ1sinμ2sinμ3τ2α1Γ(2α)+25i24sinμ1sinμ2sinμ3τ3α1Γ(3α)

    Considered α=1:

    θ(μ1,μ2,μ3,τ)=sinμ1sinμ2sinμ3[1(5iτ2)1!+(5iτ2)22!]
    θ(μ1,μ2,μ3,τ)=sinμ1sinμ2sinμ3exp(5iτ2).

    In the present section, the validity of the proposed regime is affirmed vias graphical and tabular analysis of the results. In Figure 1, approx. and exact profiles are matched at τ = 1, 2, 3, 4, and 5 for Example 1. In Figure 2, matching of approx. and exact profiles are provided at τ = 6, 7, 8, 9, and 10 for Example 1. A wide range of time levels is checked for compatibility. In Figure 3, a match of approx. and exact profiles is provided at τ = 1.0, 1.5, 2.0, 2.5 and 3.0 for Example 2. Figure 4 is related to the approx. and exact profile compatibility at τ = 1, 2, 3, 4, and 5 for Example 3. In Figure 5, compatibility of approx. and exact profiles are matched at τ = 1, 2, 3, 4, and 5 for Example 4. In Figure 6, approx.-exact compatibility is mentioned at τ = 6, 7, 8, 9, and 10 for Example 4. Figures 7 and 8 are related to the mesh-contour and surface-contour presentations at τ = 1 and τ = 2 respectively for Example 5. Figures 9 and 10 are related to the mesh-contour and surface-contour presentations at τ = 1 and τ = 2, respectively for Example 6.

    Figure 1.  Approx. and exact profiles at t = 1, 2, 3, 4 and 5 regarding Example 1.
    Figure 2.  Approx. and exact profiles at t = 6, 7, 8, 9, and 10 where N = 101 regarding Example 1.
    Figure 3.  Approx. and exact profiles at t = 1.0, 1.5, 2.0, 2.5, and 3.0 where N = 101 regarding Example 2.
    Figure 4.  Approx. and exact profiles at t = 1, 2, 3, 4, and 5 where N = 101 regarding Example 3.
    Figure 5.  Approx. and exact profiles at t = 1, 2, 3, 4, and 5 where N = 101 regarding Example 4.
    Figure 6.  Approx. and exact profiles at t = 6, 7, 8, 9, and 10 where N = 101 regarding Example 4.
    Figure 7.  Approx. and exact profiles at t = 1 where N = 51 regarding Example 5.
    Figure 8.  Approx. and exact profiles at t = 2 where N = 51 regarding Example 5.
    Figure 9.  Approx. and exact profiles at t = 1, where N = 101, z = 0.1 regarding Example 6.
    Figure 10.  Approx. and exact profiles at t = 2 where N = 101, z = 0.1 regarding Example 6.

    Via Tables 38, it can be affirmed that the approx. and exact profiles are matched for a wide range of time levels regarding the proposed scheme. Error analysis and convergence property are done by means of Tables 38. In Tables 38, L errors are provided at various grid points and time levels. Via Tables 38, it is reported that on increasing the number of grid points at different time levels, L error got reduced, and in most of the cases, convergence is affirmed up to higher order.

    In the present study, the motive is to deal with 1D, 2D, and 3D time-fractional Schr¨odinger equations regarding the approx.-analytical outcome. Shehu transform ADM is incorporated for this purpose. The prime key of a developed regime is it's easy-to-implement approach and accurate results. Furthermore, with the aid of the six mentioned examples, it is ensured that the retrieved approximated profiles are compatible with exact profiles. The graphical matching aspect between the approximated and exact results is also notified, which ensures that the developed technique is a good alternative to solve complex natured time-fractional PDEs. L error is mentioned inTables 38. Via mentioned tables, it is ensured that the proposed methodology is convergent, as on increasing the number of grid points, L error got reduced. With the aid of the developed regime, various complex natured fractional PDEs can be easily solved.

    This research received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (grant number B05F640092).

    The authors declare no conflict of interest.



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