Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

An inverse source problem for a pseudoparabolic equation with memory

  • This paper is devoted to investigating the well-posedness, as well as performing the numerical analysis, of an inverse source problem for linear pseudoparabolic equations with a memory term. The investigated inverse problem involves determining a right-hand side that depends on the spatial variable under the given observation at a final time along with the solution function. Under suitable assumptions on the problem data, the existence, uniqueness and stability of a strong generalized solution of the studied inverse problem are obtained. In addition, the pseudoparabolic problem is discretized using extended cubic B-spline functions and recast as a nonlinear least-squares minimization of the Tikhonov regularization function. Numerically, this problem is effectively solved using the MATLAB subroutine lsqnonlin. Both exact and noisy data are inverted. Numerical results for a benchmark test example are presented and discussed. Moreover, the von Neumann stability analysis is also discussed.

    Citation: M. J. Huntul, Kh. Khompysh, M. K. Shazyndayeva, M. K. Iqbal. An inverse source problem for a pseudoparabolic equation with memory[J]. AIMS Mathematics, 2024, 9(6): 14186-14212. doi: 10.3934/math.2024689

    Related Papers:

    [1] Shabir Ahmad, Aman Ullah, Mohammad Partohaghighi, Sayed Saifullah, Ali Akgül, Fahd Jarad . Oscillatory and complex behaviour of Caputo-Fabrizio fractional order HIV-1 infection model. AIMS Mathematics, 2022, 7(3): 4778-4792. doi: 10.3934/math.2022265
    [2] Gulalai, Shabir Ahmad, Fathalla Ali Rihan, Aman Ullah, Qasem M. Al-Mdallal, Ali Akgül . Nonlinear analysis of a nonlinear modified KdV equation under Atangana Baleanu Caputo derivative. AIMS Mathematics, 2022, 7(5): 7847-7865. doi: 10.3934/math.2022439
    [3] Ihsan Ullah, Aman Ullah, Shabir Ahmad, Hijaz Ahmad, Taher A. Nofal . A survey of KdV-CDG equations via nonsingular fractional operators. AIMS Mathematics, 2023, 8(8): 18964-18981. doi: 10.3934/math.2023966
    [4] Bahar Acay, Ramazan Ozarslan, Erdal Bas . Fractional physical models based on falling body problem. AIMS Mathematics, 2020, 5(3): 2608-2628. doi: 10.3934/math.2020170
    [5] Saima Rashid, Fahd Jarad, Taghreed M. Jawa . A study of behaviour for fractional order diabetes model via the nonsingular kernel. AIMS Mathematics, 2022, 7(4): 5072-5092. doi: 10.3934/math.2022282
    [6] Amir Khan, Abdur Raouf, Rahat Zarin, Abdullahi Yusuf, Usa Wannasingha Humphries . Existence theory and numerical solution of leptospirosis disease model via exponential decay law. AIMS Mathematics, 2022, 7(5): 8822-8846. doi: 10.3934/math.2022492
    [7] Muhammad Farman, Ali Akgül, Kottakkaran Sooppy Nisar, Dilshad Ahmad, Aqeel Ahmad, Sarfaraz Kamangar, C Ahamed Saleel . Epidemiological analysis of fractional order COVID-19 model with Mittag-Leffler kernel. AIMS Mathematics, 2022, 7(1): 756-783. doi: 10.3934/math.2022046
    [8] Shams A. Ahmed, Tarig M. Elzaki, Abdelgabar Adam Hassan, Husam E. Dargail, Hamdy M. Barakat, M. S. Hijazi . Applications of Elzaki transform to non-conformable fractional derivatives. AIMS Mathematics, 2025, 10(3): 5264-5284. doi: 10.3934/math.2025243
    [9] Yudhveer Singh, Devendra Kumar, Kanak Modi, Vinod Gill . A new approach to solve Cattaneo-Hristov diffusion model and fractional diffusion equations with Hilfer-Prabhakar derivative. AIMS Mathematics, 2020, 5(2): 843-855. doi: 10.3934/math.2020057
    [10] Eiman, Saowaluck Chasreechai, Thanin Sitthiwirattham, M. A. El-Shorbagy, Muhammad Sohail, Ubaid Ullah, Mati ur Rahman . Qualitative theory and approximate solution to a dynamical system under modified type Caputo-Fabrizio derivative. AIMS Mathematics, 2022, 7(8): 14376-14393. doi: 10.3934/math.2022792
  • This paper is devoted to investigating the well-posedness, as well as performing the numerical analysis, of an inverse source problem for linear pseudoparabolic equations with a memory term. The investigated inverse problem involves determining a right-hand side that depends on the spatial variable under the given observation at a final time along with the solution function. Under suitable assumptions on the problem data, the existence, uniqueness and stability of a strong generalized solution of the studied inverse problem are obtained. In addition, the pseudoparabolic problem is discretized using extended cubic B-spline functions and recast as a nonlinear least-squares minimization of the Tikhonov regularization function. Numerically, this problem is effectively solved using the MATLAB subroutine lsqnonlin. Both exact and noisy data are inverted. Numerical results for a benchmark test example are presented and discussed. Moreover, the von Neumann stability analysis is also discussed.



    Childhood infections are among the highly prevalent types of infectious diseases. Influenza, hepatitis, chickenpox, malaria, rubella and tetanus are illustrations of infections. Vaccination of children is recommended by healthcare personnel to prevent them from contracting such infections. Vaccination has had a crucial effect in diminishing the prevalence of contagious illnesses in children. On the other hand, preventable diseases constitute a significant global health hazard. Vaccination has a considerable influence on the prevention of infectious illnesses in infants. Therefore, among the most vital aspects of preventative health initiatives is childhood diseases (CHD). Numerous physicians and mathematicians are interested in the dynamics of diseases such as Haq et al. [1], Koca [2], Khan et al. [3], Ullah et al. [4] and Singh et al. [5].

    Vaccinations are the more efficient way to prevent illnesses in babies. As a result, developing a framework that anticipates the strongest performance of vaccine effectiveness needed is critical to preventing the development of such illnesses. In 1974, the World Health Organization's (WHO) "Extensive Immunization Program (EPI)" was established to expand vaccine availability for all children. Mathematical simulations are frequently employed to acquire a clearer and more comprehensive knowledge of the mechanisms of illness dissemination in children and to assess preventive approaches [6,7,8].

    The scientists employed nonlinear systems to solve real-world challenges in a variety of fields. For physicians, the concept of dynamical problems is well-known. It is gaining popularity in a variety of professions and is exploring various facets of the aforementioned domain [9,10,11,12,13,14,15]. Several experts produced fractional-order differential equations throughout the past three decades. It was widely used to overcome significant challenges in a variety of disciplines; see [16,17,18,19,20,21,22,23,24,25,26,27,28]. Existence, stability analysis, simulation and optimal approaches are some of the noted features that have lately been introduced. The fractional derivative operators were proposed by several researchers, such as Caputo [29], Podlubny [30], Hilfer [31], Baleanu et al. [32], Singh et al. [33], Kumar et al. [34] and henceforth. Atangana-Baleanu [35] recently proposed a new concept of a fractional order derivative incorporating Mittag-Leffler as a non-singular kernel, and the authors [33] highlighted the further attributes. Employing fractional derivatives and implementing them into the CHD model has resulted in a variety of scientific findings.

    All of the research aforementioned constructed a mechanistic and chaotic mathematical framework of CHD spread mechanisms, but none of them explored the fractional order feature of the paradigm. Unfortunately, no progress has been made in investigating the fractional-order behaviour of the CHD model, which correlates with the ABC fractional derivative operator up to this point.

    Our aim is that we split the disease-affected community into three epidemiological categories in the paradigm described in this article: a susceptible category (S), an infected category (I), and a removed category (R) designating inoculated as well as healed individuals having persistent immunization.

    In 2012, Arafa and other academics resurrected the classical childhood illness transmission dynamics [36]. The model is described as follows:

    {dSdϱ=(1P)νβSIνS,dIdϱ=βSI(γ+ν)I,dRdϱ=Pν+γIνR. (1.1)

    where S=s/N,I=ι/N,R=r/N,N=s+ι+r. The vaccine's efficiency is 100 percent in this scenario, and N assumed to be variable. Thus, ν represents the birth rate, β reflects the general contact rate of a vulnerable individual with an infectious person, γ represents the infected individual's recover-ability and readmission into the eliminated class, and P represents the immunized population at birth (with 0<P<1). Recently, Haq et al. [37] established the numerical solutions of fractional CHDs, Baleanu et al. [38] projected the exhibiting of prevalent CHDs via Caputo-Fabrizio in the frame of the Laplace transform, and Singh et al. [39] investigated vaccination of the SIR model.

    In 1980, Adomian first proposed the Adomian decomposition method (ADM). This approach is useful for solving systems of differential equations numerically and analytically [40,41]. The Adomian approach handles nonlinear DEs and the system of fractional DEs without requiring a Lagrange multiplier, intricate integrals, or any more variables.

    Adopting the above propensity, we use a recently developed arbitrary order derivative in the CHD model. The fundamental objective of this paper is to examine the CHD model by applying a revolutionary ABC fractional derivative operator and to describe the intricacies of the uniqueness and existence of the aforesaid model solution by aiding a fixed point postulate and the Picard–Lindelöf technique.

    The following is a summary of the considerations in the design of this article: The ABC derivative of arbitrary order is investigated in Section 2. Furthermore, the existence theory of the mechanism is presented in Section 3. An algorithm is established with the correlation of the Elzaki transform and ADM. By employing the contraction mapping theorem and the Picard–Lindelöf technique, the uniqueness and existence of the solutions to the system are analyzed in Section 4. Some simulation and tabulation consequences are debated in Section 5. Finally, in Section 6, we summarise the concluding remarks and future directions.

    The study of hypotheses and the use of derivatives and integrals of contested mappings is known as fractional calculus. In the subsequent ongoing area, we use essential terminology and findings obtained from fractional calculus theory.

    Definition 2.1. ([30]) The Caputo fractional derivative structure is stated as

    c0Dαϱ={1Γ(nα)ϱ0f(n)(x)(ϱx)α+1ndx,n1<α<n,dndϱnf(ϱ),α=n. (2.1)

    Definition 2.2. ([35]) For fH1(ˇα,ˇβ),ˇα<ˇβ,α[0,1] and The ABC fractional derivative is stated as follows:

    ABCa1Dαϱ(f(ϱ))=B(α)1αϱa1f(ϱ)Eα[α(ϱx)α1α]dx, (2.2)

    where B(α) in (2.2) is the normalization function with B(α)=B(0)=B(1)=1.

    Definition 2.3. ([35]) Let α[0,1], then the integral of fractional order α of the function f of the ABC-operator is defined as

    ABCa1Iαϱ(f(ϱ))=1αB(α)f(ϱ)+αΓ(α)B(α)ϱa1f(x)(ϱx)α1dx. (2.3)

    Definition 2.4. ([42]) A set M involving exponential function is defined as

    M={f(ϱ):z,l1,l2>0,|f(ϱ)|<ze|ϱ|li,ifϱ(1)i×[0,)|}. (2.4)

    where z is a finite number, but l1,l2 may be finite or infinite.

    Definition 2.5. ([42,43]) A mapping f(ϱ) having Elzaki transform is described as

    E{f(ϱ)}(ϑ)=U(ϑ)=ϑ0eϱϑf(ϱ)dϱ,ϱ0,ϑ[l1,l2]. (2.5)

    Theorem 2.6. ([44])(Convolution property). The subsequent consequence holds true for Elzaki transform:

    E{f1f2}=ω1E(f1)E(f2). (2.6)

    Definition 2.7. ([44]) The CFD form of Elzaki transform is described as:

    E{c0Dαϱ(f(ϱ))}(ω)=ωαU(ω)n1κ=0ω2α+κf(κ)(0),n1<α<n. (2.7)

    Definition 2.8. ([45]) The ABC fractional derivative formation of Elzaki transform is described as

    E{ABC0Dαϱ(f(ϱ))}(ω)=B(α)αωα+1α(U(ω)ωωf(0)), (2.8)

    where E{f(ϱ)}(ω)=U(ω).

    It is necessary to modify the integer-order form to arbitrary setting in order to numerically investigate the effect of biological components and to approximate the emergence and spread of CHDs. As a result, in this study, we deduce the paradigm (1.1) for the Atangana-Baleanu derivative operator supplied as

    ABCDαϱS(ϱ)=Θ1(ϱ,S(ϱ))=(1P)νβS(ϱ)I(ϱ)νS(ϱ),ABCDαϱI(ϱ)=Θ2(ϱ,I(ϱ))=βS(ϱ)I(ϱ)(γ+ν)I(ϱ),ABCDαϱR(ϱ)=Θ3(ϱ,R(ϱ))=Pν+γI(ϱ)νR(ϱ). (3.1)

    where ABCDαϱ signifies the ABC fractional derivative operator having fractional order α, i.e., 0<α1 and the ICs S0(ϱ)=S(0),I0(ϱ)=I(0),R0(ϱ)=R(0).

    In what follows, we demonstrate that (3.1) has a fixed point. To do this, we utilize (3.1) as follows:

    ABCDαϱS(ϱ)=Θ1(ϱ,S(ϱ)),ABCDαϱI(ϱ)=Θ2(ϱ,I(ϱ)),ABCDαϱR(ϱ)=Θ3(ϱ,R(ϱ)). (3.2)

    Taking into consideration the Definition 2.3 in the aforesaid system, we have

    {S(ϱ)S(0)=1αB(α)Θ1(ϱ,S(ϱ))+αB(α)Γ(α)ϱ0Θ1(ϱ,S(ϱ))(ϱx)α1dx,I(ϱ)I(0)=1αB(α)Θ2(ϱ,S(ϱ))+αB(α)Γ(α)ϱ0Θ2(ϱ,S(ϱ))(ϱx)α1dx,R(ϱ)R(0)=1αB(α)Θ3(ϱ,S(ϱ))+αB(α)Γ(α)ϱ0Θ3(ϱ,S(ϱ))(ϱx)α1dx. (3.3)

    Furthermore, we show that the kernels Θ1,Θ2,Θ3 satisfy both the Lipschitz and contraction assumptions.

    Theorem 3.1. The kernel Θ1(ϱ,S(ϱ)) preserves the Lipschitz assumptions as well as contraction if the inequality

    0βχ+ν<1 (3.4)

    is satisfied.

    Proof. Assume that Θ1(ϱ,S(ϱ))=(1P)νβS(ϱ)I(ϱ)νS(ϱ).

    For S and ˆS, we attain

    Θ1(ϱ,S(ϱ))Θ1(ϱ,ˆS(ϱ))=((1P)νβS(ϱ)I(ϱ)νS(ϱ))((1P)νβˆS(ϱ)I(ϱ)νˆS(ϱ))βI(ϱ)S(ϱ)ˆS(ϱ)+νS(ϱ)ˆS(ϱ)(βI(ϱ)+ν)S(ϱ)ˆS(ϱ)K1S(ϱ)ˆS(ϱ), (3.5)

    where K1=βχ1+ν, and I(ϱ)=maxϱJ1I(ϱ)χ1 is a bounded mapping.

    Thus, (3.5) holds the Lipschitz assumption for Θ1 and also 0βχ+ν<1, shows that Θ1 is a contraction.

    Analogously, the Lipschitz assumption for Θ2 and Θ3 are presented as follows:

    Θ2(ϱ,I(ϱ))Θ2(ϱ,ˆI(ϱ))K2I(ϱ)ˆI(ϱ),Θ3(ϱ,R(ϱ))Θ3(ϱ,ˆR(ϱ))K3R(ϱ)ˆR(ϱ), (3.6)

    where K2=βχ2+γ+ν and K3=ν and S(ϱ)=maxϱJ1S(ϱ)χ2 is a bounded mapping. Moreover, if 0βχ2+γ+ν<1 and 0ν<1, then Θ2 and Θ3 are contraction.

    Therefore, in view of kernels, (3.3) can be written in a recursive manner as follows:

    Ω1n(ϱ)=Sn(ϱ)Sn1(ϱ)=1αB(α)(Θ1(ϱ,Sn1)Θ1(ϱ,Sn2))+αB(α)Γ(α)ϱ0(Θ1(ϱ,Sn1)Θ1(ϱ,Sn2))(ϱx)α1dx,Ω2n(ϱ)=In(ϱ)In1(ϱ)=1αB(α)(Θ2(ϱ,In1)Θ2(ϱ,In2))+αB(α)Γ(α)ϱ0(Θ2(ϱ,In1)Θ2(ϱ,In2))(ϱx)α1dx,Ω3n(ϱ)=Rn(ϱ)Rn1(ϱ)=1αB(α)(Θ3(ϱ,Rn1)Θ3(ϱ,Rn2))+αB(α)Γ(α)ϱ0(Θ3(ϱ,Rn1)Θ3(ϱ,Rn2))(ϱx)α1dx, (3.7)

    supplemented by the initial conditions S0(ϱ)=S(0),I0(ϱ)=I(0) and R0(ϱ)=R(0). Applying the norm on (3.7), then we have

    Ω1n(ϱ)=Sn(ϱ)Sn1(ϱ)=1αB(α)(Θ1(ϱ,Sn1)Θ1(ϱ,Sn2))+αB(α)Γ(α)ϱ0(Θ1(ϱ,Sn1)Θ1(ϱ,Sn2))(ϱx)α1dx1αB(α)(Θ1(ϱ,Sn1)Θ1(ϱ,Sn2))+αB(α)Γ(α)ϱ0(Θ1(ϱ,Sn1)Θ1(ϱ,Sn2))(ϱx)α1dx(Bytriangularinequality). (3.8)

    In view of Lipschitz assumption (3.6), we obtain

    Ω1n(ϱ)=Sn(ϱ)Sn1(ϱ)K11αB(α)Sn1(ϱ)Sn2(ϱ)+K1αB(α)Γ(α)ϱ0Sn(ϱ)Sn1(ϱ)(ϱx)α1dx. (3.9)

    Thus, we attain

    Ω1n(ϱ)K11αB(α)Ω1(n1)(ϱ)+K1αB(α)Γ(α)ϱ0Ω1(n1)(ϱ)(ϱx)α1dx. (3.10)

    Analogously, we attain

    Ω2n(ϱ)K21αB(α)Ω2(n1)(ϱ)+K2αB(α)Γ(α)ϱ0Ω2(n1)(ϱ)(ϱx)α1dx,Ω3n(ϱ)K31αB(α)Ω3(n1)(ϱ)+K3αB(α)Γ(α)ϱ0Ω3(n1)(ϱ)(ϱx)α1dx. (3.11)

    Also, we can express that

    Sn(ϱ)=nȷ=1Ω1ȷ(ϱ),In(ϱ)=nȷ=1Ω2ȷ(ϱ),Rn(ϱ)=nȷ=1Ω3ȷ(ϱ).

    Next, to find the existence result, we surmise the subsequent theorem.

    Theorem 3.2. The CHDs model (3.1) has a solution, if there exists T such that

    1αB(α)Km+TαB(α)Γ(α)Km<1,m=1,2,3,.... (3.12)

    Proof. Since the mappings S(ϱ),I(ϱ) and R(ϱ) are bounded. Also, the kernels satisfies Lipschitz assumptions. Applying recursive technique on (3.10) and (3.11), we attain

    Ω1n(ϱ)Sn(0)[K11αB(α)+K1ϱαB(α)Γ(α)]n,Ω2n(ϱ)In(0)[K21αB(α)+K2ϱαB(α)Γ(α)]n,Ω3n(ϱ)Rn(0)[K31αB(α)+K3ϱαB(α)Γ(α)]n. (3.13)

    As a result, the mappings Ω1n(ϱ),Ω2n(ϱ) and Ω3n(ϱ) presented in (3.13) have a solution that is continuous. Furthermore, we show that the mappings presented in (3.13) enable the solutions of (3.1), we surmise that

    S(ϱ)S(0)=Sn(ϱ)Sn(ϱ),I(ϱ)I(0)=In(ϱ)In(ϱ),R(ϱ)R(0)=Rn(ϱ)Rn(ϱ), (3.14)

    where Sn(ϱ),In(ϱ) and Rn(ϱ) denotes the remainder terms of the solution.

    Therefore, we have

    Sn(ϱ)1αB(α)Θ1(ϱ,S(ϱ))Θ1(ϱ,Sn1(ϱ))+αB(α)Γ(α)ϱ0Θ1(x,S(x))Θ1(x,Sn1(x))(ϱx)α1dx1αB(α)K1SSn1+ϱαB(α)Γ(α)K1SSn1. (3.15)

    After a recursive process, we attain

    Sn(ϱ){1αB(α)+TαB(α)Γ(α)}n+1Kn+11α, (3.16)

    where α=SSn1. Applying limit on obtaining equation as n tends to , we find Sn(ϱ)0. In a similar fashion, one can evaluate that In(ϱ)0 and Rn(ϱ)0. The existense result is illustrated.

    To demonstrate the uniqueness of the solutions, we surmise that the system (3.1) has different solution such that S1(ϱ),I1(ϱ) and R1(ϱ), then we have the following:

    S(ϱ)S1(ϱ)=1αB(α)(Θ1(ϱ,S(ϱ))Θ1(ϱ,S1(ϱ)))+αB(α)Γ(α)ϱ0(Θ1(ϱ,S(ϱ))Θ1(ϱ,S1(ϱ)))(ϱx)α1dx.

    Applying norm on above equation, we have

    S(ϱ)S1(ϱ)=1αB(α)Θ1(ϱ,S(ϱ))Θ1(ϱ,S1(ϱ))+αB(α)Γ(α)ϱ0Θ1(ϱ,S(ϱ))Θ1(ϱ,S1(ϱ))(ϱx)α1dx.

    In view of Lipschitz assumptions (3.6), we have

    S(ϱ)S1(ϱ)=1αB(α)K1S(ϱ)S1(ϱ)+ϱαB(α)Γ(α)S(ϱ)S1(ϱ). (3.17)

    This yields, we have

    S(ϱ)S1(ϱ)(11αB(α)K1+ϱαB(α)Γ(α))0. (3.18)

    Theorem 3.3. The system of (3.1) has a unique solution if the subsequent assumption satisfy

    (11αB(α)K1+ϱαB(α)Γ(α))>0. (3.19)

    Proof. Assume that the hypothesis of (3.17) satisfy

    S(ϱ)S1(ϱ)(11αB(α)K1+ϱαB(α)Γ(α))0, (3.20)

    which shows that S(ϱ)S1(ϱ)=0. Hence, we conclude S(ϱ)=S1(ϱ). Analogously, we can continue the same procedure for I and R.

    In order to find the solution of the system (3.1), employing the Elzaki transform

    {E[ABCDαϱS(ϱ)](ω)=E[(1P)νβS(ϱ)I(ϱ)νS(ϱ)],E[ABCDαϱI(ϱ)]=E[βS(ϱ)I(ϱ)(γ+ν)I(ϱ)],E[ABCDαϱR(ϱ)]=E[Pν+γI(ϱ)νR(ϱ)].

    It follows that

    {B(α)ωα+1α(E[S(ϱ)]ωωS(0))=E[(1P)νβS(ϱ)I(ϱ)νS(ϱ)],B(α)ωα+1α(E[I(ϱ)]ωωI(0))=E[βS(ϱ)I(ϱ)(γ+ν)I(ϱ)],B(α)ωα+1α(E[R(ϱ)]ωωR(0))=E[Pν+γI(ϱ)νR(ϱ)].

    Now, supplementing the initial conditions and employing the inverse Elzaki transform on the aforesaid system of equations, we have

    {S(ϱ)=S(0)+E1[ωα+1αB(α)E[(1P)νβS(ϱ)I(ϱ)νS(ϱ)]],I(ϱ)=I(0)+E1[ωα+1αB(α)E[βS(ϱ)I(ϱ)(γ+ν)I(ϱ)]],R(ϱ)=R(0)+E1[ωα+1αB(α)E[Pν+γI(ϱ)νR(ϱ)]]. (4.1)

    Suppose that the infinite series formulation of S(ϱ),I(ϱ) and R(ϱ) are represented as

    S(ϱ)=n=0Sn(ϱ),I(ϱ)=n=0In(ϱ),R(ϱ)=n=0Rn(ϱ), (4.2)

    whilst the nonlinear term

    S(ϱ)I(ϱ)=n=0AN(ϱ) (4.3)

    are dealt by the Adomian polynomial stated as

    An(ϱ)=1Γ(n+1)dndαn[nκ=0ακSκ(ϱ)nκ=0ακIκ(ϱ)]α=0. (4.4)

    The first three polynomials are presented as

    An(ϱ)={S0(ϱ)I0(ϱ),n=0,S0(ϱ)I1(ϱ)+S1(ϱ)I0(ϱ),n=1,2S0(ϱ)I2(ϱ)+2S1(ϱ)I1(ϱ)+2S2(ϱ)I0(ϱ),n=2.

    plugging (4.2)–(4.4) into (4.1), we attain

    {E[n=0Sn(ϱ)]=ω2S(0)+ωα+1αB(α)E[(1P)νβn=0Sn(ϱ)n=0I(ϱ)νn=0Sn(ϱ)],E[n=0In(ϱ)]=ω2I(0)+ωα+1αB(α)E[βn=0Sn(ϱ)n=0In(ϱ)(γ+ν)n=0In(ϱ)],E[n=0Rn(ϱ)]=ω2R(0)+ωα+1αB(α)E[Pν+γn=0Inνn=0Rn(ϱ)]. (4.5)

    Comparing both sides of (4.5) presents the subsequent recursive algorithm, we have

    E[S0]=ω2S(0),E[S1]=ωα+1αB(α)[ω2(1P)νβE[A0(ϱ)]νE[S0(ϱ)]],E[S2]=ωα+1αB(α)[ω2(1P)νβE[A1(ϱ)]νE[S(ϱ)]],E[Sn+1]=ωα+1αB(α)[ω2(1P)νβE[An(ϱ)]νE[Sn(ϱ)]],E[I0]=ω2I(0),E[I1]=ωα+1αB(α)[βE[A0(ϱ)](γ+ν)E[I0(ϱ)]],E[I2]=ωα+1αB(α)[βE[A1(ϱ)](γ+ν)E[I1(ϱ)]],E[In+1]=ωα+1αB(α)[βE[An(ϱ)](γ+ν)E[In(ϱ)]],E[R0]=ω2R(0),E[R1]=ωα+1αB(α)[ω2Pν+γE[I0(ϱ)]νE[R0(ϱ)]],E[R2]=ωα+1αB(α)[ω2Pν+γE[I1(ϱ)]νE[R1(ϱ)]],E[Rn+1]=ωα+1αB(α)[ω2Pν+γE[In(ϱ)]νE[Rn(ϱ)]]. (4.6)

    By employing the inverse Elzaki transform, we attain the iterative terms as follows:

    S0=S0,S1=(1P)νB(α)[αϱαΓ(α+1)+(1α)]β(S0I0+νI0)B(α)[αϱαΓ(α+1)+(1α)](1P)ν2B2(α)[α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2],S2=(1P)νB(α)[αϱαΓ(α+1)+(1α)]β2(S20I0+β(γ+ν)S0I0)B2(α)×[α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2]β(S0I0+S0ν)B2(α)[α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2](1P)ν2(βI0+ν)B3(α)×[α3ϱ3αΓ(3α+1)+3α2(1α)ϱ2αΓ(2α+1)+3α(1α)2ϱαΓ(α+1)+(1α)3],I0=I0,I1=βS0I0B(α)[αϱαΓ(α+1)+(1α)](γ+ν)B(α)[αϱαΓ(α+1)+(1α)],I2=βS0I0B2(α)(βS0I0(γ+ν)(γ+ν)βS0I0(γ+ν)I0)×[α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2]βI0(βS0I0+S0ν)B2(α)[α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2](1P)ν2(βI0+ν)B3(α)×[α3ϱ3αΓ(3α+1)+3α2(1α)ϱ2αΓ(2α+1)+3α(1α)2ϱαΓ(α+1)+(1α)3],R0=R0,R1=PνγI0γR0B(α)[αϱαΓ(α+1)+(1α)]PνB2(α)[α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2],R2=PνB(α)[αϱαΓ(α+1)+(1α)]+γ(βS0I0(γ+ν))+(νγI0ν2R0)B2(α)×[α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2]Pν2B3(α)×[α3ϱ3αΓ(3α+1)+3α2(1α)ϱ2αΓ(2α+1)+3α(1α)2ϱαΓ(α+1)+(1α)3]. (4.7)

    Analogously, one can achieve the additional components. Hence, the infinite series solution of (3.1) is presented as:

    {S(ϱ)=S0(ϱ)+S1(ϱ)+S2(ϱ)+...,I(ϱ)=I0(ϱ)+I1(ϱ)+I2(ϱ)+...,R(ϱ)=R0(ϱ)+R1(ϱ)+R2(ϱ)+.... (4.8)

    In this analysis, we utilize S0=1,I0=0.5,R0=0,ν=0.4,β=0.8,γ=0.03,P1=0.9 and the EADM yields an approximate solution in the form of an infinite series. Thus, we compute the first four terms of (4.8) as follows:

    S(ϱ)=10.75520B(α)(αϱαΓ(α+1)+(1α))0.446080B2(α)(α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2)0.01280B3(α)(α3ϱ3αΓ(3α+1)+3α2(1α)ϱ2αΓ(2α+1)+3α(1α)2ϱαΓ(α+1)+(1α)3),I(ϱ)=0.5+0.185B(α)(αϱαΓ(α+1)+(1α))0.48680B2(α)(α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2)+0.784B3(α)(α3ϱ3αΓ(3α+1)+3α2(1α)ϱ2αΓ(2α+1)+3α(1α)2ϱαΓ(α+1)+(1α)3),R(ϱ)=0.345B(α)(αϱαΓ(α+1)+(1α))0.360720B2(α)(α2ϱ2αΓ(2α+1)+2α(1α)ϱαΓ(α+1)+(1α)2)0.14400B3(α)(α3ϱ3αΓ(3α+1)+3α2(1α)ϱ2αΓ(2α+1)+3α(1α)2ϱαΓ(α+1)+(1α)3). (4.9)

    In Figure 1, the estimated solutions of several segments versus the provided data and pertaining to various fractional orders have been shown. Furthermore, the outcomes reveal that the response is constantly contingent on the time-fractional derivative and parametric settings. According to inoculation, the prevalence of the cured group progressively improves as the number of the vulnerable group diminishes. As shown in Figure 1, for a short period of time, responsiveness diminishes, resulting in a reduction in infection, as illustrated in Figure 2. Figure 3 depicts a typical case of glucose intolerance. This can be seen in the graphs for excessive plasma concentrations, where despite considerable increases in adjusted glucose metabolism concentrations in the liver, glycogen synthesis generation could be completely inhibited. The fractional-order derivative corresponds to the growth and contraction of distinct components. For a short period of time, the involved procedure is rapid on a small fractional order, but subsequently it reverses and remains sluggish on the identical lesser fractional order. As a result, in biological models, the fractional calculus depicts systemic mechanisms of deterioration and regeneration. The diffraction peaks and their associated vibrational frequencies are identical across scenarios; however, the timeframe of these peaks varies. Within fractional orders, the investigated model's transitory vibrations are considerably more spread out than those of its integer-order counterpart, whereby responses have prolonged interepidemic intervals. As a consequence, we arrived at the conclusion that, relying on the attribute values, the fractional-order framework simultaneously cumulates to the classical model and matches statistics equally, or incorporates data more accurately and excels the conventional paradigm.

    Figure 1.  Simulation of susceptible group S(ϱ) for different fractional order α=1,0.9,0.8,0.7,0.6.
    Figure 2.  Simulation of infected group I(ϱ) for different fractional order α=1,0.9,0.8,0.7,0.6.
    Figure 3.  Simulation of removed group R(ϱ) for different fractional order α=1,0.9,0.8,0.7,0.6.

    Tables 13 exhibit the findings of the model of epidemic CHDs using Caputo, Caputo-Fabrizio, and ABC fractional derivatives for the susceptible class(S), infected class(I), and removed class(R), respectively. The summary statistics of the local stability investigations are supported by exisiting results provided by Haq et al. [37] and Baleanu et al. [38]. We proceeded by presenting several standard stability conclusions for a simplistic formula, as well as their application to a scenario in which the framework was reinforced substantially feasible by including a category of incubating persons and a group of biologically protective children.

    Table 1.  Analysis among the solutions of the CHDs model in the Caputo fractional derivative CDα [37], Caputo-Fabrizio CFDα [38] and Atangana-Baleanu fractional derivative in the Caputo sense ABCDα for S(ϱ).
    ϱ CDα(α=0.7) CFDα(α=0.7) ABCDα(α=0.7) CDα(α=0.9) CFDα(α=0.9) ABCDα(α=0.9)
    0.1 5.3254×101 4.9673×101 4.6457×101 9.5907×101 8.9987×101 8.0459×101
    0.2 4.6534×101 3.9846×101 3.2426×101 8.2342×102 7.8945×102 7.0285×101
    0.3 7.7789×101 6.7789×101 6.0087×101 2.5673×101 1.9999×101 1.9807×101
    0.4 5.0000×101 4.9999×101 4.9727×101 8.0099×102 8.0000×102 7.9055×102
    0.5 4.0060×101 3.9889×101 3.5590×102 4.0000×101 3.9999×101 3.9146×101
    0.6 2.3452×101 1.9445×101 1.4732×101 3.2378×102 2.9889×102 2.8317×101
    0.7 3.8809×101 2.8998×101 2.5703×101 2.3334×101 1.9344×102 1.7227×101
    0.8 4.0092×101 3.9987×101 3.6520×101 6.0001×102 5.9991×102 5.8529×102
    0.9 5.3423×101 4.9924×101 4.7263×101 6.0022×102 5.9992×102 5.8002×102
    1.0 6.8870×101 5.9991×101 5.7886×101 2.0090×101 1.9939×101 1.7730×101

     | Show Table
    DownLoad: CSV
    Table 2.  Analysis among the solutions of the CHDs model in the Caputo fractional derivative CDα [37], Caputo-Fabrizio CFDα [38] and Atangana-Baleanu fractional derivative in the Caputo sense ABCDα for I(ϱ).
    ϱ CDα(α=0.7) CFDα(α=0.7) ABCDα(α=0.7) CDα(α=0.9) CFDα(α=0.9) ABCDα(α=0.9)
    0.1 6.0023×101 5.8871×101 5.2585×101 6.3465×101 5.8912×101 5.5334×101
    0.2 6.1200×101 5.9233×101 5.3392×101 7.1022×101 5.9909×101 5.7221×101
    0.3 6.9982×101 5.9833×101 5.4100×101 6.1122×101 5.9999×101 5.9330×101
    0.4 6.0100×101 5.8842×101 5.4820×101 7.8881×101 6.7723×101 6.1708×101
    0.5 6.3889×101 5.9844×101 5.5639×101 7.0821×101 6.8891×101 6.4370×101
    0.6 6.3241×101 5.9999×101 5.6613×101 7.2312×101 6.7778×101 6.7328×101
    0.7 6.1188×101 5.8800×101 5.7821×101 8.1817×101 7.7623×101 7.0587×101
    0.8 6.0000×101 5.9999×101 5.9318×101 8.0023×101 7.9988×101 7.4152×101
    0.9 7.7478×101 6.9999×101 6.1157×101 8.8000×101 8.0001×101 7.8025×101
    1.0 7.4458×101 6.9901×101 6.3394×101 9.3114×101 8.8891×101 8.2213×101

     | Show Table
    DownLoad: CSV
    Table 3.  Analysis among the solutions of the CHDs model in the Caputo fractional derivative CDα [37], Caputo-Fabrizio CFDα [38] and Atangana-Baleanu fractional derivative in the Caputo sense ABCDα for R(ϱ).
    ϱ CDα(α=0.7) CFDα(α=0.7) ABCDα(α=0.7) CDα(α=0.9) CFDα(α=0.9) ABCDα(α=0.9)
    0.1 2.6611×101 1.9911×101 1.7711×101 2.0001×101 1.9590×101 1.4498×101
    0.2 3.0012×101 2.9345×101 2.0212×101 3.0079×101 2.9988×101 2.0347×101
    0.3 3.9345×101 2.9999×101 2.1549×101 3.2000×101 2.9563×101 2.5365×101
    0.4 3.0012×101 2.8921×101 2.2101×101 3.5663×101 2.9912×101 2.9702×101
    0.5 3.0003×101 2.9900×101 2.2038×101 4.0000×101 3.9982×101 3.3429×101
    0.6 3.1101×101 3.9999×101 2.1461×101 4.0044×101 3.9899×101 3.6584×101
    0.7 3.0081×101 3.9900×101 2.0431×101 5.0000×101 4.1231×101 3.9192×101
    0.8 2.5622×101 2.0077×101 1.8996×101 5.0012×101 4.8912×101 4.1267×101
    0.9 2.0001×101 3.4491×101 1.7189×101 5.0099×101 4.9888×101 4.2818×101
    1.0 2.6702×101 2.5556×101 1.5031×101 5.6540×101 5.0034×101 4.3856×101

     | Show Table
    DownLoad: CSV

    Theorem 5.1. System of Eq (3.1) has a solutions by employing the Picard–Lindelöf technique.

    Proof. By means of kernels defined in (3.2), we have

    Θ1(ϱ,S(ϱ))=(1P)νβS(ϱ)I(ϱ)νS(ϱ),Θ2(ϱ,I(ϱ))=βS(ϱ)I(ϱ)(γ+ν)I(ϱ),Θ3(ϱ,R(ϱ))=Pν+γI(ϱ)νR(ϱ), (5.1)

    where Θ1(ϱ,S(ϱ)),Θ2(ϱ,I(ϱ)) and Θ3(ϱ,R(ϱ)) are contraction mappings according to (3.6), respectively.

    Assume that

    Υ1=supQa1,B|Θ1(ϱ,S(ϱ))|,Υ2=supQa1,b2|Θ1(ϱ,S(ϱ))|,Υ3=supQa1,b3|Θ1(ϱ,S(ϱ))| (5.2)

    where

    Qa1,b1=|ϱa1,ϱ+a1|×[Θ1b1,Θ1+b1]=W1×Y1,
    Qa1,b2=|ϱa1,ϱ+a1|×[Θ2b2,Θ2+b2]=W1×Y2,
    Qa1,b3=|ϱa1,ϱ+a1|×[Θ2b3,Θ2+b3]=W1×Y3.

    Taking the Picard operator, we have

    Φ:Q(W1,Y1,Y2,Y3)Q(W1,Y1,Y2,Y3), (5.3)

    presented as follows:

    ΦΞ(ϱ)=Ξ0(ϱ)+Δ(ϱ,Θ(ϱ))1αB(α)+αB(α)Γ(α)ϱ0(ϱx)α1Δ(x,Θ(x))dx, (5.4)

    where Ξ(ϱ)={S(ϱ),I(ϱ),R(ϱ)},Ξ0(ϱ)={ζ1,ζ2,ζ3} and Δ(ϱ,Θ(ϱ))={Θ1(ϱ,S(ϱ)),Θ2(ϱ,I(ϱ)),Θ3(ϱ,R(ϱ))}.

    Next, we surmise that the solutions of the fractional CHD model are bounded in a specified time domain,

    Ξ(ϱ)max{Θ1,Θ2,Θ3}Ξ(ϱ)Ξ0(ϱ)=Δ(ϱ,Θ(ϱ))1αB(α)+αB(α)Γ(α)ϱ0(ϱx)α1Δ(x,Θ(x))dx1αB(α)Δ(ϱ,Θ(ϱ))+αB(α)Γ(α)ϱ0(ϱx)α1Δ(x,Θ(x))dx(1αB(α)+ζϱαB(α)Γ(α))max{Y1,Y2,Y3}ρζY, (5.5)

    then we have

    ρ<Yζ.

    Taking into account the fixed point postulate via the Banach space with the metric, we get

    ΦΞ1ΦΞ2=supϱW1|Ξ1Ξ2|. (5.6)

    Now we have

    ΦΞ1ΦΞ2=[Δ(ϱ,Ξ1(ϱ))Δ(ϱ,Ξ2(ϱ))]1αB(α)+αB(α)Γ(α)ϱ0(ϱx)α1[Δ(x,Ξ1(x))Δ(x,Ξ2(x))]dx1αB(α)Δ(ϱ,Ξ1(ϱ))Δ(ϱ,Ξ2(ϱ))+αB(α)Γ(α)ϱ0(ϱx)α1Δ(x,Ξ1(x))Δ(x,Ξ2(x))dx1αB(α)ζΞ1(ϱ)Ξ2(ϱ)+ϱαζB(α)Γ(α)Ξ(x)Ξ2(x)(1αB(α)ζ+ϱαζB(α)Γ(α))Ξ1(x)Ξ2(x)ρζΞ1(x)Ξ2(x),ζ<1. (5.7)

    Since Ξ is a contraction and ρζ<1. Also, the introduced operator, Φ is contraction. Thus, the CHD model concerning the ABC derivative presented in (3.1) has a fixed point.

    Assume that (φ,.) denotes a Banach space and U signifies a self-map of φ. Also, suppose that n+1=Ψ(U,n) be a recursive scheme. Surmise that a fixed point set (U) of U has at least one element. Suppose that n is convergent and tends to z1(U). If {n}φ and introducing an=n+1Ψ(U,n) and if limnan1=0 gives the outcome limnn=z1, then the recursive approach n+1=Ψ(U,n) is said to be U-stable.

    Theorem 5.2. ([46]) Assume that a Banach space (φ,.) and U be a self-map of the Banach space φ yields the outcome

    UxUy˜HxUx+xy,x,yφ, (5.8)

    where ˜H and such that [0,1],˜H0. Also, surmise that U is Picard U-stable.

    We surmise that the following recursive formulae have an association with the fractional CHD model (3.1)

    {Sn+1(ϱ)=S(ϱ)+E1[ωα+1αB(α)E[(1P)νβSn(ϱ)In(ϱ)νSn(ϱ)]],In+1(ϱ)=I(ϱ)+E1[ωα+1αB(α)E[βSn(ϱ)In(ϱ)(γ+ν)In(ϱ)]],Rn+1(ϱ)=R(ϱ)+E1[ωα+1αB(α)E[Pν+γIn(ϱ)νRn(ϱ)]].

    The term ωα+1αB(α) considered to be the Lagrange multiplier in the aforesaid system.

    In the next result, we present the following theorem.

    Theorem 5.3. Assume that the self-map G is represented as follows:

    {G(Sn(ϱ))=Sn+1(ϱ)=S(ϱ)+E1[ωα+1αB(α)E[(1P)νβSn(ϱ)In(ϱ)νSn(ϱ)]],G(In(ϱ))=In+1(ϱ)=I(ϱ)+E1[ωα+1αB(α)E[βSn(ϱ)In(ϱ)(γ+ν)In(ϱ)]],G(Rn(ϱ))=Rn+1(ϱ)=R(ϱ)+E1[ωα+1αB(α)E[Pν+γIn(ϱ)νRn(ϱ)]],

    then the recursive system are G stable in L2(a1,B) if (1P)νβφ4(ϱ)νφ1(ϱ)<1,βφ4(ϱ)(γ+ν)φ2(ϱ)<1 and Pν+γφ2(ϱ)νφ3(ϱ)<1.

    Proof. Initially, we prove that G has a fixed point. To prove the above consequence, we obtain the outcome presented below for (n,m)B×B.

    G(Sn(ϱ))G(Sm(ϱ))Sn(ϱ)Sm(ϱ)+E1[ωα+1αB(α)E[(1P)νβ(Sn(ϱ)In(ϱ)Sm(ϱ)Im(ϱ))ν(Sn(ϱ)Sm(ϱ))]],G(In(ϱ))G(Im(ϱ))In(ϱ)Im(ϱ)+E1[ωα+1αB(α)E[β(Sn(ϱ)In(ϱ)Sm(ϱ)Im(ϱ))(γ+ν)(In(ϱ)Im(ϱ))]] (5.9)

    and

    G(Rn(ϱ))G(Rm(ϱ))Rn(ϱ)Rm(ϱ)+E1[ωα+1αB(α)E[Pν+γ(In(ϱ)Im(ϱ))ν(Rn(ϱ)Rm(ϱ))]]. (5.10)

    Also, we have that

    Sn(ϱ)Sm(ϱ)In(ϱ)Im(ϱ)Rn(ϱ)Rm(ϱ)Sn(ϱ)In(ϱ)Sm(ϱ)Im(ϱ). (5.11)

    By plugging (5.11) in (5.9) and (5.10), we have

    G(Sn(ϱ))G(Sm(ϱ))((1P)νβφ4(ϱ)νφ1(ϱ))Sn(ϱ)Sm(ϱ),G(In(ϱ))G(Im(ϱ))(βφ4(ϱ)(γ+ν)φ2(ϱ))In(ϱ)Im(ϱ) (5.12)

    and

    G(Rn(ϱ))G(Rm(ϱ))(Pν+γφ2(ϱ)νφ3(ϱ))Rn(ϱ)Rm(ϱ), (5.13)

    where φi,i=1,....,4, are mappings arising from E1[ωα+1αB(α)E(˙)] along with ((1P)νβφ4(ϱ)νφ1(ϱ))<1,(βφ4(ϱ)(γ+ν)φ2(ϱ))<1 and (Pν+γφ2(ϱ)νφ3(ϱ))<1.

    As a result, the self-map G has a fixed point. Moreover, the assumptions connected with the Theorem 5.2 are satisfied by G. Now, suppose that (5.12) and (5.13) hold, thus we have

    ˜H={((1P)νβφ4(ϱ)νφ1(ϱ)),(βφ4(ϱ)(γ+ν)φ2(ϱ)),(Pν+γφ2(ϱ)νφ3(ϱ)). (5.14)

    It is noteworthy that in map G the supposition concerned with Theorem 5.2 satisfied. Thus, for the supposed map, G and the hypothesis relating to Theorem 5.2 are satisfied, hence G is Picard-stable. This completes the proof.

    In this study, the generalized fractional derivative is implemented to examine the mathematical model of CHDs. Also, with the assistance of the contraction mapping theorem, we demonstrated that the response of the proposed model (3.1) exists and has at least one solution. The Picard–Lindelöf approach with the self-map U used to validate the solution's stability. Furthermore, using the EADM algorithm, we comprehended the approximate results for various fractional-orders using the Matlab/Maple application, and we concluded that the results of the projected model (3.1) approach the classical solution when α1. As a result, it can be concluded that EADM is a controllable, simplistic, and more powerful analytical process for analyzing linear/non-linear phenomena. The findings of this research are extremely beneficial to healthcare professionals who interact with peditric and associated concerns. Consequently, we deduce that the ABC derivative can be used to investigate the physical, pharmacological, physiological, medicinal, sociological, and economic systems.

    This research was supported by Taif University Research Supporting Project Number (TURSP-2020/164), Taif University, Taif, Saudi Arabia.

    The authors declare that they have no competing interests.



    [1] A. B. Al'shin, M. O. Korpusov, A. G. Siveshnikov, Blow-up in nonlinear Sobolev type equations, De Gruyter Series in Nonlinear Analysis and Applications, 2011. https://doi.org/10.1515/9783110255294
    [2] S. N. Antontsev, J. I. Diaz, S. Shmarev, Energy methods for free boundary problems: Applications to nonlinear PDEs and fluid mechanics, Progress in Nonlinear Differential Equations and their Applications 48, Birkhäuser, 2002. https://doi.org/10.1115/1.1483358
    [3] M. Amin, M. Abbas, D. Baleanu, M. K. Iqbal, M. B. Riaz, Redefined extended cubic B-spline functions for numerical solution of time-fractional telegraph equation, CMES Comp. Model. Eng., 127 (2021), 361–384. https://doi.org/10.32604/cmes.2021.012720 doi: 10.32604/cmes.2021.012720
    [4] M. Amin, M. Abbas, M. K. Iqbal, D. Baleanu, Numerical treatment of time-fractional Klein-Gordon equation using redefined extended cubic B-spline functions, Front. Phys., 8 (2020), 288. https://doi.org/10.3389/fphy.2020.00288 doi: 10.3389/fphy.2020.00288
    [5] S. N. Antontsev, S. E. Aitzhanov, G. R. Ashurova, An inverse problem for the pseudo-parabolic equation with p-Laplacian, EECT, 11 (2022), 399–414. https://doi.org/10.3934/eect.2021005 doi: 10.3934/eect.2021005
    [6] A. Asanov, E. R. Atamanov, Nonclassical and inverse problems for pseudoparabolic equations, De Gruyter, Berlin, 1997. https://doi.org/10.1515/9783110900149
    [7] E. S. Dzektser, Generalization of the equation of motion of ground waters with free surface, Dokl. Akad. Nauk SSSR, 202 (1972), 1031–1033.
    [8] V. E. Fedorov, A. V. Urasaeva, An inverse problem for linear Sobolev type equations, J. Inverse III-Pose. P., 12 (2004), 387–395. https://doi.org/10.1163/1569394042248210 doi: 10.1163/1569394042248210
    [9] M. Gholamian, J. Saberi-Nadjafi, Cubic B-splines collocation method for a class of partial integro-differential equation, Alex. Eng. J., 57 (2018), 2157–2165. https://doi.org/10.1016/j.aej.2017.06.004 doi: 10.1016/j.aej.2017.06.004
    [10] H. A. Hammad, H. U. Rehman, H. Almusawa, Tikhonov regularization terms for accelerating inertial Mann-Like algorithm with applications, Symmetry, 13 (2021), 554. https://doi.org/10.3390/sym13040554 doi: 10.3390/sym13040554
    [11] M. J. Huntul, M. Tamsir, N. Dhiman, Identification of time-dependent potential in a fourth-order pseudo-hyperbolic equation from additional measurement, Math. Method. Appl. Sci., 45 (2022), 5249–5266. https://doi.org/10.1002/mma.8104 doi: 10.1002/mma.8104
    [12] M. J. Huntul, N. Dhiman, M. Tamsir, Reconstructing an unknown potential term in the third-order pseudo-parabolic problem, Comput. Appl. Math., 40 (2021), 140.
    [13] M. J. Huntul, M. Tamsir, N. Dhiman, An inverse problem of identifying the time-dependent potential in a fourth-order pseudo-parabolic equation from additional condition, Numer. Meth. Part. D. E., 39 (2023), 848–865. https://doi.org/10.1002/num.22778 doi: 10.1002/num.22778
    [14] M. J. Huntul, Identifying an unknown heat source term in the third-order pseudo-parabolic equation from nonlocal integral observation, Int. Commun. Heat Mass, 128 (2021), 105550. https://doi.org/10.1016/j.icheatmasstransfer.2021.105550 doi: 10.1016/j.icheatmasstransfer.2021.105550
    [15] M. J. Huntul, Recovering a source term in the higher-order pseudo-parabolic equation via cubic spline functions, Phys. Scr., 97 (2022), 035004. https://doi.org/10.1088/1402-4896/ac54d0 doi: 10.1088/1402-4896/ac54d0
    [16] K. Kenzhebai, An inverse problem of recovering the right hand side of 1D pseudoparabolic equation, JMCS, 111 (2021), 28–37. https://doi.org/10.26577/JMMCS.2021.v111.i3.03 doi: 10.26577/JMMCS.2021.v111.i3.03
    [17] K. Khompysh, Inverse problem for 1D pseudo-parabolic equation, FAIA, 216 (2017), 382–387. https://doi.org/10.1007/978-3-319-67053-9_36 doi: 10.1007/978-3-319-67053-9_36
    [18] K. Khompysh, A. G. Shakir, The inverse problem for determining the right part of the pseudo-parabolic equation, JMCS, 105 (2020), 87–98. https://doi.org/10.26577/JMMCS.2020.v105.i1.08 doi: 10.26577/JMMCS.2020.v105.i1.08
    [19] N. Khalid, M. Abbas, M. K. Iqbal, D. Baleanu, A numerical investigation of Caputo time fractional Allen-Cahn equation using redefined cubic B-spline functions, Adv. Differ. Equ., 2020 (2020), 1–22. https://doi.org/10.1186/s13662-020-02616-x doi: 10.1186/s13662-020-02616-x
    [20] A. Y. Kolesov, E. F. Mishchenko, N. K. Rozov, Asymptotic methods of investigation of periodic solutions of nonlinear hyperbolic equations, T. Mat. I. Imeni V.A.S., 222 (1998), 3–191.
    [21] M. O. Korpusov, A. G. Sveshnikov, Three-dimensional nonlinear evolution equations of pseudoparabolic type in problems of mathematical physics, Z. V. Mat. I Mat. F., 43 (2003), 1835–1869.
    [22] J. L. Lions, Quelques methodes de resolution des problemes aux limites non-liniaires, Paris, Dunod, 1969.
    [23] A. S. Lyubanova, Inverse problem for a pseudoparabolic equation with integral overdetermination conditions, Differ. Equ., 50 (2014), 502–512. https://doi.org/10.1134/S0012266114040089 doi: 10.1134/S0012266114040089
    [24] Mathworks, Documentation optimization toolbox-least squares (model fitting) algorithms, 2020. Available from: www.mathworks.com.
    [25] Y. T. Mehraliyev, G. K. Shafiyeva, Determination of an unknown coefficient in the third order pseudoparabolic equation with non-self-adjoint boundary conditions, J. Appl. Math., 2014 (2014), 1–7. https://doi.org/10.1155/2014/358696 doi: 10.1155/2014/358696
    [26] Y. T. Mehraliyev, A. T. Ramazanova, M. J. Huntul, An inverse boundary value problem for a two-dimensional pseudo-parabolic equation of third order, Results Appl. Math., 14 (2022), 100274. https://doi.org/10.1016/j.rinam.2022.100274 doi: 10.1016/j.rinam.2022.100274
    [27] N. Mshary, Exploration of nonlinear traveling wave phenomena in quintic conformable Benney-Lin equation within a liquid film, AIMS Math., 9 (2024), 11051–11075. https://doi.org/10.3934/math.2024542 doi: 10.3934/math.2024542
    [28] A. P. Oskolkov, Uniqueness and global solvability for boundary value problems for the equations of motion of water solutions of polymers, Zap. Nauchn. Sem. POMI, 38 (1973), 98–136.
    [29] A. I. Prilepko, D. G. Orlovsky, I. A. Vasin, Methods for solving inverse problems in mathematical physics, Marcel Dekker, New York, Basel, 2000.
    [30] P. Rosenau, Evolution and breaking of ion-acoustic waves, Phys. Fluids, 31 (1988), 1317–1319. https://doi.org/10.1063/1.866723 doi: 10.1063/1.866723
    [31] W. Rundell, Determination of an unknown nonhomogeneous term in a linear partial differential equation from overspecified boundary data, Appl. Anal., 10 (1980), 231–242. https://doi.org/10.1080/00036818008839304 doi: 10.1080/00036818008839304
    [32] B. K. Shivamoggi, A symmetric regularized long‐wave equation for shallow water waves, Phys. Fluids, 29 (1986), 890–891. https://doi.org/10.1063/1.865895 doi: 10.1063/1.865895
    [33] R. E. Showalter, T. W. Ting, Pseudoparabolic partial differential equations, SIAM, 1 (1970), 1–26. https://doi.org/10.1137/0501001 doi: 10.1137/0501001
    [34] M. Tamsir, D. Nigam, N. Dhiman, A. Chauhan, A hybrid B-spline collocation technique for the Caputo time fractional nonlinear Burgers' equation, Beni-Suef U. J. Basic, 12 (2023), 95. https://doi.org/10.1186/s43088-023-00434-0 doi: 10.1186/s43088-023-00434-0
    [35] V. G. Zvyagin, M. V. Turbin, The study of initial-boundary value problems for mathematical models of the motion of Kelvin-Voigt fluids, J. Math. Sci., 168 (2012), 157–308. https://doi.org/10.1007/s10958-010-9981-2 doi: 10.1007/s10958-010-9981-2
    [36] H. Zhang, X. Han, X. Yang, Quintic B-spline collocation method for fourth order partial integro-differential equations with a weakly singular kernel, Appl. Math. Comput., 219 (2013), 6565–6575. https://doi.org/10.1016/j.amc.2013.01.012 doi: 10.1016/j.amc.2013.01.012
  • This article has been cited by:

    1. Saima Rashid, Rehana Ashraf, Fahd Jarad, Strong interaction of Jafari decomposition method with nonlinear fractional-order partial differential equations arising in plasma via the singular and nonsingular kernels, 2022, 7, 2473-6988, 7936, 10.3934/math.2022444
    2. Fouad Mohammad Salama, Nur Nadiah Abd Hamid, Umair Ali, Norhashidah Hj. Mohd Ali, Fast hybrid explicit group methods for solving 2D fractional advection-diffusion equation, 2022, 7, 2473-6988, 15854, 10.3934/math.2022868
    3. Fouad Mohammad Salama, On Numerical Simulations of Variable-Order Fractional Cable Equation Arising in Neuronal Dynamics, 2024, 8, 2504-3110, 282, 10.3390/fractalfract8050282
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1195) PDF downloads(87) Cited by(2)

Figures and Tables

Figures(5)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog