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Numerical investigation of convergence in the L norm for modified SGFEM applied to elliptic interface problems

  • Convergence in the L norm is a very important consideration in numerical simulations of interface problems. In this paper, a modified stable generalized finite element method (SGFEM) was proposed for solving the second-order elliptic interface problem in the two-dimensional bounded and convex domain. The proposed SGFEM uses a one-side enrichment function. There is no stability term in the weak form of the model problem, and it is a conforming finite element method. Moreover, it is applicable to any smooth interface, regardless of its concavity or shape. Several nontrivial examples illustrate the excellent properties of the proposed SGFEM, including its convergence in both the L2 and L norms, as well as its stability and robustness.

    Citation: Pengfei Zhu, Kai Liu. Numerical investigation of convergence in the L norm for modified SGFEM applied to elliptic interface problems[J]. AIMS Mathematics, 2024, 9(11): 31252-31273. doi: 10.3934/math.20241507

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  • Convergence in the L norm is a very important consideration in numerical simulations of interface problems. In this paper, a modified stable generalized finite element method (SGFEM) was proposed for solving the second-order elliptic interface problem in the two-dimensional bounded and convex domain. The proposed SGFEM uses a one-side enrichment function. There is no stability term in the weak form of the model problem, and it is a conforming finite element method. Moreover, it is applicable to any smooth interface, regardless of its concavity or shape. Several nontrivial examples illustrate the excellent properties of the proposed SGFEM, including its convergence in both the L2 and L norms, as well as its stability and robustness.



    Fractional differential equations (FDEs) have a profound physical background and rich theoretical connotations and have been particularly eye-catching in recent years. Fractional order differential equations refer to equations that contain fractional derivatives or integrals. Currently, fractional derivatives and integrals have a wide range of applications in many disciplines such as physics, biology, and chemistry, etc. For more information see [1,2,3,45].

    Langevin equation is an important tool of many areas such as mathematical physics, protein dynamics [6], deuteron-cluster dynamics, and described anomalous diffusion [7]. In 1908, Langevin established first the Langevin equation with a view to describe the advancement of physical phenomena in fluctuating conditions [8]. Some evolution processes are characterized by the fact that they change of state abruptly at certain moments of time. These perturbations are short-term in comparison with the duration of the processes. So, the Langevin equations are a suitable tool to describe such problems. Besides the intensive improvement of fractional derivatives, the Langevin (FDEs) have been presented in 1990 by Mainardi and Pironi [9], which was trailed by numerous works interested in some properties of solutions like existence and uniqueness for Langevin FDEs [10,11,12,13,14,15,16,17,18,19]. We also refer here to some recent works that deal with a qualitative analysis of such problems, including the generalized Hilfer operator, see [20,21,22,23,24]. Recent works related to our work were done by [25,26,27,28,29,30]. The monotone iterative technique is one of the important techniques used to obtain explicit solutions for some differential equations. For more details about the monotone iterative technique, we refer the reader to the classical monographs [31,32].

    Lakshmikantham and Vatsala [25] studied the general existence and uniqueness results for the following FDE

    {Dμ0+(υ(ϰ)υ(0))=f(ϰ,υ(ϰ)),ϰ[0,b],υ(0)=υ0,

    by the monotone iterative technique and comparison principle. Fazli et al. [26] investigated the existence of extremal solutions of a nonlinear Langevin FDE described as follows

    {Dμ10+(Dμ20++λ)υ(ϰ)=f(ϰ,υ(ϰ)),ϰ[0,b],g(υ(0),υ(b))=0,Dμ20+υ(0)=υμ2,

    via a constructive technique that produces monotone sequences that converge to the extremal solutions. Wang et al. [27], used the monotone iterative method to prove the existence of extremal solutions for the following nonlinear Langevin FDE

    {βDμ0+(γDμ0++λ)υ(ϰ)=f(ϰ,υ(ϰ),(γDμ0++λ)),ϰ(0,b],ϰμ(1γ)υ(0)=τ1η0υ(s)ds+mi=1μiυ(σi),ϰμ(1β)(γDμ0++λ)υ(0)=τ2η0 γDμ0+υ(s)ds+mi=1ργiDμ0+υ(σi),

    Motivated by the novel advancements of the Langevin equation and its applications, also by the above argumentations, in this work, we apply the monotone iterative method to investigate the lower and upper explicit monotone iterative sequences that converge to the extremal solution of a fractional Langevin equation (FLE) with multi-point sub-strip boundary conditions described by

    {(HDμ1,β1;ϕ0++λ1)(HDμ2,β2;ϕ0++λ2)υ(ϰ)=f(ϰ,υ(ϰ)),ϰ(0,b],HDμ2,β2;ϕ0+υ(ϰ)|ϰ=0=0,υ(0)=0,υ(b)=mi=1δiIσi,ϕ0+υ(ζi),  (1.1)

    where HDμ1,β1;ϕ0+  and HDμ2,β2;ϕ0+ are the ϕ-Hilfer fractional derivatives of order  μ1(0,1]  and μ2(1,2] respectively, and type β1,β2[0,1],σi>0,λ1,λ2R+, δi>0, m1, 0<ζ1<ζ2<......<1, f:(0,b]×RR is a given continuous function and ϕ is an increasing function, having a continuous derivative ϕ on (0,b) such that ϕ(ϰ)0, for all ϰ(0,b]. Our main contributions to this work are as follows:

    By adopting the same techniques used in [26,27], we derive the formula of explicit solutions for ϕ-Hilfer-FLEs (1.1) involving two parameters Mittag-Leffler functions.

    We use the monotone iterative method to study the extremal of solutions of ϕ-Hilfer-FLE (1.1).

    We investigate the lower and upper explicit monotone iterative sequences that converge to the extremal solution.

    The proposed problem (1.1) covers some problems involving many classical fractional derivative operators, for different values of function ϕ and parameter μi,i=1,2. For instance:

    If ϕ(ϰ)=ϰ and μi=1, then the FLE (1.1) reduces to Caputo-type FLE.

    If ϕ(ϰ)=ϰ and μi=0, then the FLE (1.1) reduces to Riemann-Liouville-type FLE.

    If μi=0, then the FLE (1.1) reduces to FLE with the ϕ-Riemann-Liouville fractional derivative.

    If ϕ(ϰ)=ϰ, then the FLE (1.1) reduces to classical Hilfer-type FLE.

    If ϕ(ϰ)=logϰ, then the FLE (1.1) reduces to Hilfer-Hadamard-type FLE.

    If ϕ(ϰ)=ϰρ, then the FLE (1.1) reduces to Katugampola-type FLE.

    The results obtained in this work includes the results of Fazli et al. [26], Wang et al. [27] and cover many problems which do not study yet.

    The structure of our paper is as follows: In the second section, we present some notations, auxiliary lemmas and some basic definitions which are used throughout the paper. Moreover, we derive the formula of the explicit solution for FLE (1.1) in the term of Mittag-Leffler with two parameters. In the third section, we discuss the existence of extremal solutions to our FLE (1.1) and prove lower and upper explicit monotone iterative sequences which converge to the extremal solution. In the fourth section, we provide a numerical example to illustrate the validity of our results. The concluding remarks will be given in the last section.

    To achieve our main purpose, we present here some definitions and basic auxiliary results that are required throughout our paper. Let J:=[0,b], and C(J) be the Banach space of continuous functions  υ:JR equipped with the norm υ=sup{|υ(ϰ)|:ϰJ}.

    Definition 2.1. [2] Let f  be an integrable function and μ>0. Also, let ϕ be an increasing and positive monotone function on (0,b), having a continuous derivative ϕ on (0,b) such that ϕ(ϰ)0, for all ϰJ. Then the ϕ-Riemann-Liouville fractional integral of f of order μ is defined by

    Iμ,ϕ0+f(ϰ)=ϰ0ϕ(s)(ϕ(ϰ)ϕ(s))μ1Γ(μ)f(s)ds, 0<ϰb.

    Definition 2.2. [33] Let n1<μ<n, (nN), and f,ϕCn(J) such that ϕ(ϰ) is continuous and satisfying ϕ(ϰ)0 for all ϰJ. Then the left-sided ϕ-Hilfer fractional derivative of a function f of order μ and type β[0,1] is defined by

    HDμ,β,ϕ0+f(ϰ)=Iβ(nμ);ϕ0+Dγ;ϕa+f(ϰ),γ=μ+nβμβ,

    where

    Dγ;ϕ0+f(ϰ)=f[n]ϕI(1β)(nμ);ϕ0+f(ϰ),andf[n]ϕ=[1ϕ(ϰ)ddϰ]n.

    Lemma 2.3. [2,33] Let n1<μ<n, 0β1, and n<δR. For a given function f:JR, we have

    Iμ,ϕ0+Iβ,ϕ0+f(ϰ)=Iμ+β,ϕ0+f(ϰ),
    Iμ,ϕ0+(ϕ(ϰ)ϕ(0))δ1=Γ(δ)Γ(μ+δ)(ϕ(ϰ)ϕ(0))μ+δ1,

    and

    HDμ,β,ϕ0+(ϕ(ϰ)ϕ(0))δ1=0,δ<n.

    Lemma 2.4. [33] Let f:JR, n1<μ<n, and 0β1. Then

    (1) If fCn1(J), then

    Iμ;ϕ0+HDμ,β,ϕ0+f(ϰ)=f(ϰ)n1k=1(ϕ(ϰ)ϕ(0))γkΓ(γk+1)f[nk]ϕI(1β)(nμ);ϕ0+f(0),

    (2) If fC(J), then

    HDμ,β,ϕ0+Iμ;ϕ0+f(ϰ)=f(ϰ).

    Lemma 2.5. For μ,β,γ>0 and λR, we have

    Iμ,ϕ0+[ϕ(ϰ)ϕ(0)]β1Eγ,β[λ(ϕ(ϰ)ϕ(0))γ]=[ϕ(ϰ)ϕ(0)]β+μ1Eγ,β+μ[λ(ϕ(ϰ)ϕ(0))γ],

    where Eγ,β is Mittag-Leffler function with two-parameterdefined by

    Eγ,β(υ)=i=1υiΓ(γi+β),υC.

    Proof. See [34].

    Lemma 2.6. [27] Let μ(1,2] and β>0 be arbitrary. Then the functions Eμ(), Eμ,μ() and Eμ,β() are nonnegative. Furthermore,

    Eμ(χ):=Eμ,1(χ)1,Eμ,μ(χ)1Γ(μ),Eμ,β(χ)1Γ(β),

    for χ<0.

    Lemma 2.7. Let μ,k,β>0, λR and fC(J). Then

    Ik,ϕ0+[Iμ,ϕ0+Eμ,μ(λ(ϕ(ϰ)ϕ(0))μ)]=Iμ+k,ϕ0+Eμ,μ+k(λ(ϕ(ϰ)ϕ(0))μ).

    Proof. See [34].

    For some analysis techniques, we will suffice with indication to the classical Banach contraction principle (see [35]).

    To transform the ϕ-Hilfer type FLE (1.1) into a fixed point problem, we will present the following Lemma.

    Lemma 2.8. Let γj=μj+jβjμjβj, (j=1,2) such that μ1(0,1],μ2(1,2], βj[0,1],λ1,λ20 and   is a functionin the space C(J). Then, υ is a solutionof the ϕ-Hilfer linear FLE of the form

    {(HDμ1,β1;ϕ0++λ1)(HDμ2,β2;ϕ0++λ2)v(ϰ)=(ϰ),ϰ(0,b],HDμ2,β2;ϕ0+v(ϰ)|ϰ=0=0,v(0)=0,v(b)=mi=1δiIσi,ϕ0+v(ζi), (2.1)

    if and only if υ satisfies the following equation

    υ(ϰ)=[ϕ(ϰ)ϕ(0)]γ21Eμ2,γ2(λ2[ϕ(ϰ)ϕ(0)]μ2)Θ[Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(b)ϕ(0)]μ2)(Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(b)ϕ(0)]μ1)(b))mi=1δiΓ(μ2)Iμ2+σi,ϕ0+Eμ2,μ2+σi(λ2[ϕ(ζi)ϕ(0)]μ2)(Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(ζi)ϕ(0)]μ1)(ζi))]+Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(ϰ)ϕ(0)]μ2)Γ(μ1)Iμ1,ϕ0+[Eμ1,μ1(λ1[ϕ(ϰ)ϕ(0)]μ1)(ϰ)]. (2.2)

    where

    Θ:=(mi=1δi[ϕ(ζi)ϕ(0)]γ2+σi1Eμ2,γ2+σi(λ2[ϕ(ζi)ϕ(0)]μ2)[ϕ(b)ϕ(0)]γ21Eμ2,γ2(λ2[ϕ(b)ϕ(0)]μ2))0. (2.3)

    Proof. Let (HDμ2,β2;ϕ0++λ2)υ(ϰ)=P(ϰ). Then, the problem (2.1) is equivalent to the following problem

    {(HDμ1,β1;ϕ0++λ1)P(ϰ)=(ϰ),ϰ(0,b],P(0)=0.      (2.4)

    Applying the operator Iμ1,ϕ0+ to both sides of the first equation of (2.4) and using Lemma 2.4, we obtain

    P(ϰ)=c0Γ(γ1)[ϕ(ϰ)ϕ(0)]γ11λ1Iμ1,ϕ0+P(ϰ)+Iμ1,ϕ0+(ϰ), (2.5)

    where c0 is an arbitrary constant. For explicit solutions of Eq (2.4), we use the method of successive approximations, that is

    P0(ϰ)=c0Γ(γ1)[ϕ(ϰ)ϕ(0)]γ11, (2.6)

    and

    Pk(ϰ)=P0(ϰ)λ1Iμ1,ϕ0+Pk1(ϰ)+Iμ1,ϕ0+(ϰ). (2.7)

    By Definition 2.1 and Lemma 2.3 along with Eq (2.6), we obtain

    P1(ϰ)=P0(ϰ)λ1Iμ1,ϕ0+P0(ϰ)+Iμ1,ϕ0+(ϰ)=c0Γ(γ1)[ϕ(ϰ)ϕ(0)]γ11λ1Iμ1,ϕ0+(c0Γ(γ1)[ϕ(ϰ)ϕ(0)]γ11)+Iμ1,ϕ0+(ϰ)=c0Γ(γ1)[ϕ(ϰ)ϕ(0)]γ11λ1c0Γ(γ1+μ1)[ϕ(ϰ)ϕ(0)]γ1+μ11+Iμ1,ϕ0+(ϰ)=c02i=1(λ1)i1[ϕ(ϰ)ϕ(0)]iμ1+β1(1μ1)1Γ(iμ1+β1(1μ1))+Iμ1,ϕ0+(ϰ). (2.8)

    Similarly, by using Eqs (2.6)–(2.8), we get

    P2(ϰ)=P0(ϰ)λ1Iμ1,ϕ0+P1(ϰ)+Iμ1,ϕ0+(ϰ)=c0Γ(γ1)[ϕ(ϰ)ϕ(0)]γ11λ1Iμ1,ϕ0+(c02i=1(λ1)i1[ϕ(ϰ)ϕ(0)]iμ1+β1(1μ1)1Γ(iμ1+β1(1μ1))+Iμ1,ϕ0+(ϰ))+Iμ1,ϕ0+(ϰ)=c03i=1(λ1)i1[ϕ(ϰ)ϕ(0)]iμ1+β1(1μ1)1Γ(iμ1+β1(1μ1))+2i=1(λ1)i1Iiμ1,ϕ0+(ϰ).

    Repeating this process, we get Pk(ϰ) as

    Pk(ϰ)=c0k+1i=1(λ1)i1[ϕ(ϰ)ϕ(0)]iμ1+β1(1μ1)1Γ(iμ1+β1(1μ1))+ki=1(λ1)i1Iiμ1,ϕ0+(ϰ).

    Taking the limit k, we obtain the expression for Pk(ϰ), that is

    P(ϰ)=c0i=1(λ1)i1[ϕ(ϰ)ϕ(0)]iμ1+β1(1μ1)1Γ(iμ1+β1(1μ1))+i=1(λ1)i1Iiμ1,ϕ0+(ϰ).

    Changing the summation index in the last expression, ii+1, we have

    P(ϰ)=c0i=0(λ1)i[ϕ(ϰ)ϕ(0)]iμ1+γ11Γ(iμ1+γ1)+i=0(λ1)iIiμ1+μ1,ϕ0+(ϰ).

    From the definition of Mittag-Leffler function, we get

    P(ϰ)=c0[ϕ(ϰ)ϕ(0)]γ11Eμ1,γ1(λ1[ϕ(ϰ)ϕ(0)]μ1)+Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(ϰ)ϕ(0)]μ1)(ϰ). (2.9)

    By the condition P(0)=0, we get c0=0 and hence

    Equation (2.9) reduces to

    P(ϰ)=Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(ϰ)ϕ(0)]μ1)(ϰ). (2.10a)

    Similarly, the following equation

    {(HDμ2,β2;ϕ0++λ2)υ(ϰ)=P(ϰ),ϰ(0,b],υ(0)=0,υ(b)=mi=1δiIσi,ϕ0+υ(ζi)

    is equivalent to

    υ(ϰ)=c1[ϕ(ϰ)ϕ(0)]γ21Eμ2,γ2(λ2[ϕ(ϰ)ϕ(0)]μ2)+c2[ϕ(ϰ)ϕ(0)]γ22Eμ2,γ21(λ2[ϕ(ϰ)ϕ(0)]μ2)+Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(ϰ)ϕ(0)]μ2)P(ϰ). (2.11)

    By the condition υ(0)=0, we obtain c2=0 and hence Eq (2.11) reduces to

    υ(ϰ)=c1[ϕ(ϰ)ϕ(0)]γ21Eμ2,γ2(λ2[ϕ(ϰ)ϕ(0)]μ2)+Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(ϰ)ϕ(0)]μ2)P(ϰ). (2.12)

    By the condition υ(b)=mi=1δi Iσi,ϕ0+υ(ζi), we get

    c1=1Θ(Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(b)ϕ(0)]μ2)P(b)mi=1δiΓ(μ2)Iμ2+σi,ϕ0+Eμ2,μ2+σi(λ2[ϕ(ζi)ϕ(0)]μ2)P(ζi)). (2.13)

    Put c0 in Eq (2.12), we obtain

    υ(ϰ)=[ϕ(ϰ)ϕ(0)]γ21Eμ2,γ2(λ2[ϕ(ϰ)ϕ(0)]μ2)Θ[Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(b)ϕ(0)]μ2)P(b)mi=1δiΓ(μ2)Iμ2+σi,ϕ0+Eμ2,μ2+σi(λ2[ϕ(ζi)ϕ(0)]μ2)P(ζi)]+Γ(μ2)Iμ2,ϕ0+[Eμ2,μ2(λ2[ϕ(ϰ)ϕ(0)]μ2)P(ϰ)]. (2.14)

    Substituting Eq (2.10a) into Eq (2.14), we can get Eq (2.2).

    On the other hand, we assume that the solution υ satisfies Eq (2.2). Then, one can get υ(0)=0. Applying HDμ2,β2;ϕ0+ on both sides of Eq (2.2), we get

    HDμ2,β2;ϕ0+υ(ϰ)=HDμ2,β2;ϕ0+[ϕ(ϰ)ϕ(0)]γ21Eμ2,γ2(λ2[ϕ(ϰ)ϕ(0)]μ2)Θ[Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(b)ϕ(0)]μ2)(Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(b)ϕ(0)]μ1)(b))mi=1δiΓ(μ2)Iμ2+σi,ϕ0+Eμ2,μ2+σi(λ2[ϕ(ζi)ϕ(0)]μ2)(Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(ζi)ϕ(0)]μ1)(ζi))]+HDμ2,β2;ϕ0+Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(ϰ)ϕ(0)]μ2)Γ(μ1)Iμ1,ϕ0+[Eμ1,μ1(λ1[ϕ(ϰ)ϕ(0)]μ1)(ϰ)]. (2.15)

    Since γ2=μ2+β2μ2β2, then, by Lemma 2.3, we have HDμ2,β2;ϕ0+[ϕ(ϰ)ϕ(0)]γ21=0 and hence Eq (2.15) reduces to the following equation

    HDμ2,β2;ϕ0+υ(ϰ)=HDμ2,β2;ϕ0+Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(ϰ)ϕ(0)]μ2)Γ(μ1)Iμ1,ϕ0+[Eμ1,μ1(λ1[ϕ(ϰ)ϕ(0)]μ1)(ϰ)].

    By using some properties of Mittag-Leffler function and taking ϰ=0, we obtain

    HDμ2,β2;ϕ0+υ(0)=0.

    Thus, the derivative condition is satisfied. The proof of Lemma 2.8 is completed.

    Lemma 2.9. (Comparison Theorem). For j=1,2, let γj=μj+jβjμjβj, μ1(0,1],μ2(1,2], βj[0,1],λ10 and  υC(J) be acontinuous function satisfies

    {(HDμ1,β1;ϕ0++λ1)(HDμ2,β2;ϕ0++λ2)v(ϰ)0,HDμ2,β2;ϕ0+v(ϰ)|ϰ=00,v(0)0,v(b)0,

    then υ(ϰ)0, ϰ(0,b].

    Proof. If z0, then from Lemma 2.6, we have Eμ,β(z)0. If z<0, then Eμ,β(z) is completely monotonic function [35], that means Eμ,β(z) possesses derivatives for all arbitrary integer order and (1)ndndznEμ,β(z)0. Hence, Eμ,β(z)0 for all zR. In view of Eq (2.2), Eq (2.9), and from fact that Eμ1,γ1()0 and Eμ,μ()0 with help the definition of ϕ, we obtain υ(ϰ)0, for ϰ(0,b]. (Alternative proof). Let (HDμ2,β2;ϕ0++λ2)υ(ϰ)=P(ϰ). Then, we have

    {(HDμ1,β1;ϕ0++λ1)P(ϰ)0,P(0)0.

    Assume that P(ϰ)0 (for all ϰ(0,b]) is not true. Then, there exist ϰ1,ϰ2, (0<ϰ1<ϰ2b) such that P(ϰ2)<0,P(ϰ1)=0 and

    {P(ϰ)0,ϰ(0,ϰ1),P(ϰ)<0,ϰ(ϰ1,ϰ2).

    Since λ10, we have (HDμ1,β1;ϕ0++λ1)P(ϰ)0 for all ϰ(ϰ1,ϰ2). In view of

    HDμ1,β1,ϕ0+P(ϰ)=Iβ1(1μ1);ϕ0+(1ϕ(ϰ)ddϰ)I1γ1;ϕ0+P(ϰ),

    the operator I1γ1;ϕ0+P(ϰ) is nondecreasing on (ϰ1,ϰ2). Hence

    I1γ1;ϕ0+P(ϰ)I1γ1;ϕ0+P(ϰ1)0,ϰ(ϰ1,ϰ2).

    On the other hand, for all ϰ(ϰ1,ϰ2), we have

    I1γ1;ϕ0+P(ϰ)I1γ1;ϕ0+P(ϰ1)=1Γ(1γ1)ϰ0ϕ(s)(ϕ(ϰ)ϕ(s))1γ11P(s)ds1Γ(1γ1)ϰ10ϕ(s)(ϕ(ϰ1)ϕ(s))1γ11P(s)ds=1Γ(1γ1)ϰ10ϕ(s)[(ϕ(ϰ)ϕ(s))γ1(ϕ(ϰ1)ϕ(s))γ1]P(s)ds+1Γ(1γ1)ϰϰ1ϕ(s)(ϕ(ϰ)ϕ(s))γ1P(s)ds<0, for all ϰ(ϰ1,ϰ2),

    which is a contradiction. Therefore, P(ϰ)0 (ϰ(0,b]). By the same technique, one can prove that υ(ϰ)0, for all ϰ(0,b].

    As a result of Lemma 2.8, we have the following Lemma.

    Lemma 2.10. For j=1,2, let γj=μj+jβjμjβj, μ1(0,1],μ2(1,2], βj[0,1]  and f:J×RR is continuous function . If υC(J) satisfies the problem (1.1), then, υ satisfies thefollowing integral equation

    υ(ϰ)=[ϕ(ϰ)ϕ(0)]γ21Eμ2,γ2(λ2[ϕ(ϰ)ϕ(0)]μ2)Θ[Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(b)ϕ(0)]μ2)(Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(b)ϕ(0)]μ1)f(b,υ(b)))mi=1δiΓ(μ2)Iμ2+σi,ϕ0+Eμ2,μ2+σi(λ2[ϕ(ζi)ϕ(0)]μ2)(Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(ζi)ϕ(0)]μ1)f(ζi,υ(ζi)))]+Γ(μ2)Iμ2,ϕ0+Eμ2,μ2(λ2[ϕ(ϰ)ϕ(0)]μ2)(Γ(μ1)Iμ1,ϕ0+Eμ1,μ1(λ1[ϕ(ϰ)ϕ(0)]μ1)f(ϰ,υ(ϰ))).

    In this part, we focus on the existence of lower and upper explicit monotone iterative sequences that converge to the extremal solution for the nonlinear ϕ-Hilfer FLE (1.1). The existence of unique solution for the problem (1.1) is based on Banach fixed point theorem. Now, let us give the following definitions:

    Definition 3.1. For J= [0,b]R+. Let υC(J). Then, the upper and lower-control functions are defined by

    ¯f(ϰ,υ(ϰ))=sup0Yυ{f(ϰ,Y(ϰ))},

    and

    f_(ϰ,υ(ϰ))=infυYb{f(ϰ,Y(ϰ))},

    respectively. Clearly, ¯f(ϰ,υ(ϰ)) and f_(ϰ,υ(ϰ)) are monotonous non-decreasing on [a,b] and

    f_(ϰ,υ(ϰ))f(ϰ,υ(ϰ))¯f(ϰ,υ(ϰ))

    Definition 3.2. Let ¯υ, υ_ C(J) be upper and lower solutions of the problem (1.1) respectively. Then

    {(HDμ1,β1;ϕ0++λ1)(HDμ2,β2;ϕ0++λ2)¯υ(ϰ)¯f(ϰ,¯υ(ϰ)),ϰ(0,b],HDμ2,β2;ϕ0+¯υ(ϰ)|ϰ=00,¯υ(0)0,¯υ(b)mi=1δiIσi,ϕ0+¯υ(ζi),

    and

    {(HDμ1,β1;ϕ0++λ1)(HDμ2,β2;ϕ0++λ2)υ_(ϰ)f_(ϰ,υ_(ϰ)),ϰ(0,b],HDμ2,β2;ϕ0+υ_(ϰ)|ϰ=00,υ_(0)0,υ_(b)mi=1δiIσi,ϕ0+υ_(ζi).

    According to Lemma 2.8, we have

    \begin{eqnarray*} \overline{\upsilon }(\varkappa ) &\geq &\frac{\left[ \phi (\varkappa )-\phi (0)\right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) f(b, \overline{\upsilon }(b)\right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( \lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) f(\zeta _{i}, \overline{\upsilon }(\zeta _{i}))\right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) f(\varkappa, \overline{\upsilon }(\varkappa ))\right) \end{eqnarray*}

    and

    \begin{eqnarray*} \underline{\upsilon }(\varkappa ) &\leq &\frac{\left[ \phi (\varkappa )-\phi (0)\right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) f(b, \underline{\upsilon }(b)\right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( -\lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) f(\zeta _{i}, \underline{\upsilon }(\zeta _{i}))\right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) f(\varkappa, \underline{\upsilon }(\varkappa ))\right). \end{eqnarray*}

    Theorem 3.3. Let \overline{\upsilon }(\varkappa) and \underline{\upsilon }(\varkappa) be upper and lower solutions of the problem (1.1), respectively such that \underline{ \upsilon } \left(\varkappa \right)\leq \overline{\upsilon }\left(\varkappa \right) on \mathcal{J}. Moreover, the function f\left(\varkappa, \upsilon \right) is continuouson \mathcal{J} and there exists a constant number \kappa > 0 such that \left\vert f\left(\varkappa, \upsilon \right) -f\left(\varkappa, v\right)\right\vert \leq \kappa \left\vert \upsilon -v\right\vert, for \upsilon, v\in \mathbb{R} ^{+}, \varkappa \in \mathcal{J} . If

    \begin{eqnarray*} Q_{1} & = &\kappa \frac{\left[ \phi (b)-\phi (0)\right] ^{\gamma _{2}-1}}{ \Gamma \left( \gamma _{2}\right) \Theta }\left[ \frac{\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}+\mu _{1}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) }\right. \\ &&\left. +\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) \frac{\left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}+\mu _{1}+\sigma _{i}}}{\Gamma \left( \mu _{2}+\sigma _{i}+1\right) \Gamma \left( \mu _{2}+\sigma _{i}\right) \Gamma \left( \mu _{1}+1\right) }\right] \\ &&+\kappa \frac{\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}+\mu _{1}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) } < 1, \end{eqnarray*}

    then the problem (1.1) has a unique solution \upsilon \in C\left(\mathcal{J}\right).

    Proof. Let \Xi = P- \underline{ P }, where P(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \upsilon (\varkappa)\ and \underline{P}(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{\upsilon }(\varkappa). Then, we get

    \begin{equation*} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \Xi \geq 0, \;\varkappa \in (0, b], \\ \Xi (0) = 0. \ \ \end{array} \right. \end{equation*}

    In view of Lemma 2.9, we have \Xi \left(\varkappa \right) \geq 0 on \mathcal{J}\ and hence \underline{ P } \left(\varkappa \right) \leq P\left(\varkappa \right) . Since P(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \upsilon (\varkappa)\ and \underline{P}(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{\upsilon }(\varkappa), by the same technique, we get \underline{ \upsilon } \left(\varkappa \right) \leq \upsilon \left(\varkappa \right) . Similarly, we can show that \upsilon \left(\varkappa \right) \leq \overline{\upsilon }\left(\varkappa \right). Consider the continuous operator \mathcal{G}:\mathcal{C}\left(\mathcal{J} \right) \rightarrow \mathcal{C}\left(\mathcal{J}\right) defined by

    \begin{eqnarray*} \mathcal{G}\upsilon (\varkappa ) & = &\frac{\left[ \phi (\varkappa )-\phi (0) \right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) f\left( b, \upsilon (b)\right) \right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( -\lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) f\left( \zeta _{i}, \upsilon (\zeta _{i})\right) \right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) f\left( \varkappa, \upsilon (\varkappa )\right) \right). \end{eqnarray*}

    Clearly, the fixed point of \mathcal{G} is a solution to problem (1.1). Define a closed ball \mathbb{B}_{R} as

    \begin{equation*} \mathbb{B}_{R} = \left\{ \upsilon \in \mathcal{C}\left( \mathcal{J}\right) :\left\Vert \upsilon \right\Vert _{\mathcal{C}\left( \mathcal{J}\right) }\leq R, \right\} \end{equation*}

    with

    \begin{equation*} R\geq \frac{Q_{2}}{1-Q_{1}}, \end{equation*}

    where

    \begin{eqnarray*} &&Q_{2} = \mathcal{P}\frac{\left[ \phi (b)-\phi (0)\right] ^{\gamma _{2}-1}}{ \Gamma \left( \gamma _{2}\right) \Theta }\left[ \frac{\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}+\mu _{1}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) }\right. \\ &&\left. +\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) \frac{\left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}+\mu _{1}+\sigma _{i}}}{\Gamma \left( \mu _{2}+\sigma _{i}+1\right) \Gamma \left( \mu _{2}+\sigma _{i}\right) \Gamma \left( \mu _{1}+1\right) }\right] \\ &&+\mathcal{P}\frac{\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}+\mu _{1}}}{ \Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) } \end{eqnarray*}

    and \mathcal{P} = \sup_{s\in \mathcal{J}}\left\vert f(s, 0)\right\vert. Let \upsilon \in \mathbb{B}_{R} and \varkappa \in \mathcal{J} . Then by Lemma 2.6, we have

    \begin{eqnarray*} \left\vert f\left( \varkappa, \upsilon (\varkappa )\right) \right\vert & = &\left\vert f\left( \varkappa, \upsilon (\varkappa )\right) -f\left( \varkappa, 0\right) +f\left( \varkappa, 0\right) \right\vert \\ &\leq &\left\vert f\left( \varkappa, \upsilon (\varkappa )\right) -f\left( \varkappa, 0\right) \right\vert +\left\vert f\left( \varkappa, 0\right) \right\vert \\ &\leq &\kappa \left\vert \upsilon (\varkappa )\right\vert +\mathcal{P} \\ &\leq &\left( \kappa \left\Vert \upsilon \right\Vert +\mathcal{P}\right). \end{eqnarray*}

    Now, we will present the proof in two steps:

    \textsf{First step:} We will show that \mathcal{G}(\mathbb{B}_{R})\subset \mathbb{B}_{R}. First, by Lemma 2.6 and Definition 2.1, we have

    \begin{equation*} I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( \lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \leq \frac{\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{2}\right) }. \end{equation*}

    Next, for \upsilon \in \mathbb{B}_{R} , we obtain

    \begin{eqnarray*} &&\left\vert \mathcal{G}\upsilon \left( \varkappa \right) \right\vert \\ &&\left. \leq \right. \frac{\left[ \phi (b)-\phi (0)\right] ^{\gamma _{2}-1} }{\Gamma \left( \gamma _{2}\right) \Theta }\left[ \left( \kappa \left\Vert \upsilon \right\Vert +\mathcal{P}\right) \frac{\left[ \phi (b)-\phi (0) \right] ^{\mu _{2}+\mu _{1}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) }\right. \\ &&\left. \left. +\right. \sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) \frac{\left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}+\mu _{1}+\sigma _{i}}}{\Gamma \left( \mu _{2}+\sigma _{i}+1\right) \Gamma \left( \mu _{2}+\sigma _{i}\right) \Gamma \left( \mu _{1}+1\right) }\left( \kappa \left\Vert \upsilon \right\Vert +\mathcal{P}\right) \right] \\ &&\left. +\right. \left( \kappa \left\Vert \upsilon \right\Vert +\mathcal{P} \right) \frac{\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}+\mu _{1}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) } \\ &&\left. \leq \right. Q_{1}R+Q_{2} \\ &&\left. \leq \right. R. \end{eqnarray*}

    Thus \mathcal{G}(\mathbb{B}_{R})\subset \mathbb{B}_{R}.

    \textsf{Second step:} We shall prove that \mathcal{G} is contraction. Let \upsilon, \widehat{\upsilon }\in \mathbb{B}_{R} and \varkappa \in \mathcal{ J} . Then by Lemma 2.6 and Definition 2.1, we obtain

    \begin{eqnarray*} \left\Vert \mathcal{G}\upsilon -\mathcal{G}\widehat{\upsilon }\right\Vert &\leq &\kappa \left\Vert \upsilon -\widehat{\upsilon }\right\Vert \frac{ \left( \phi (b\varkappa )-\phi (0)\right) ^{\gamma _{2}-1}}{\Gamma \left( \gamma _{2}\right) \Theta }\left[ \frac{\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}+\mu _{1}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) }\right. \\ &&\left. +\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) \frac{\left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}+\mu _{1}+\sigma _{i}}}{\Gamma \left( \mu _{2}+\sigma _{i}+1\right) \Gamma \left( \mu _{2}+\sigma _{i}\right) \Gamma \left( \mu _{1}+1\right) }\right] \\ &&+\kappa \left\Vert \upsilon -\widehat{\upsilon }\right\Vert \frac{\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}+\mu _{1}}}{\Gamma \left( \mu _{2}+1\right) \Gamma \left( \mu _{1}+1\right) } \\ &\leq &Q_{1}\left\Vert \upsilon -\widehat{\upsilon }\right\Vert. \end{eqnarray*}

    Thus, \mathcal{G} is a contraction. Hence, the Banach contraction principle theorem [35] shows that the problem (1.1) has a unique solution.

    Theorem 3.4. Assume that \overline{\upsilon }, \underline{\upsilon }\in C\left(\mathcal{J}\right) be upper and lower solutions of the problem (1.1), respectively, and \underline{ \upsilon } \left(\varkappa \right)\leq \overline{\upsilon }\left(\varkappa \right) on \mathcal{\ J} . Inaddition, If the continuous function f:\mathcal{\ J}\times \mathbb{R} \rightarrow \mathbb{R} satisfies f\left(\varkappa, \upsilon \left(\varkappa \right) \right)\leq f\left(\varkappa, y\left(\varkappa \right) \right) for all \underline{ \upsilon } \left(\varkappa \right) \leq \upsilon \left(\varkappa \right) \leq y(\varkappa)\leq \overline{\upsilon }\left(\varkappa \right), \varkappa \in \mathcal{\ J} then there exist monotoneiterative sequences \left\{ \underline{\upsilon }_{j}\right\}_{j = 0}^{\infty } and \left\{ \overline{\upsilon }_{j}\right\}_{j = 0}^{\infty } which uniformly converges on \mathcal{J} to the extremal solutions of problem (1.1) in \Phi = \left\{ \upsilon \in \mathcal{C}\left(\mathcal{J}\right) :\underline{\upsilon }\left(\varkappa \right)\leq \upsilon \left(\varkappa \right) \leq \overline{\upsilon }\left(\varkappa \right), \varkappa \in \mathcal{J}\right\}.

    Proof. \textsf{Step (1):} Setting \underline{\upsilon }_{0} = \underline{\upsilon } and \overline{\upsilon }_{0} = \overline{\upsilon } , then given \left\{ \underline{\upsilon }_{j}\right\} _{j = 0}^{\infty } and \left\{ \overline{ \upsilon }_{j}\right\} _{j = 0}^{\infty } inductively define \underline{ \upsilon }_{j+1} and \overline{\upsilon }_{j+1} to be the unique solutions of the following problem

    \begin{equation} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{ \upsilon }_{j+1}(\varkappa ) = f\left( \varkappa, \underline{\upsilon } _{j}(\varkappa )\right) , \;\varkappa \in \mathcal{J}, \ \ \ \ \ \ \ \\ \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\underline{\upsilon } _{j+1}(\varkappa )\right\vert _{\varkappa = 0} = 0, \underline{\upsilon } _{j+1}(0) = 0, \underline{\upsilon }_{j+1}(b) = \sum_{i = 1}^{m}\delta _{i}I_{0^{+}}^{\sigma _{i}, \phi }\underline{\upsilon }_{j+1}(\zeta _{i}). \end{array} \right. \end{equation} (3.1)

    and

    \begin{equation} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \overline{ \upsilon }_{j+1}(\varkappa ) = f\left( \varkappa, \overline{\upsilon } _{j}(\varkappa )\right) , \;\varkappa \in \mathcal{J}, \ \ \ \ \ \ \ \\ \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\overline{\upsilon } _{j+1}(\varkappa )\right\vert _{\varkappa = 0} = 0, \overline{\upsilon } _{j+1}(0) = 0, \overline{\upsilon }_{j+1}(b) = \sum_{i = 1}^{m}\delta _{i}I_{0^{+}}^{\sigma _{i}, \phi }\overline{\upsilon }_{j+1}(\zeta _{i}). \end{array} \right. \end{equation} (3.2)

    By Theorem 3.3, we know that the above problems have a unique solutions in \mathcal{C}\left(\mathcal{J}\right) .

    \textsf{Step (2):} Now, for \varkappa \in \mathcal{J}, we claim that

    \begin{eqnarray} \underline{\upsilon }(\varkappa ) & = &\underline{\upsilon }_{0}(\varkappa )\leq \underline{\upsilon }_{1}(\varkappa )\leq........\leq \underline{ \upsilon }_{j}(\varkappa )\leq \underline{\upsilon }_{j+1}(\varkappa ) \\ &\leq &......\leq \overline{\upsilon }_{j+1}(\varkappa )\leq \overline{ \upsilon }_{j}(\varkappa )\leq......\leq \overline{\upsilon }_{1}(\varkappa )\leq \overline{\upsilon }_{0}(\varkappa ) = \overline{\upsilon }(\varkappa ). \end{eqnarray} (3.3)

    To confirm this claim, from (3.1) for j = 0, we have

    \begin{equation} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{ \upsilon }_{1}(\varkappa ) = f\left( \varkappa, \underline{\upsilon } _{0}(\varkappa )\right) , \;j\geq 0, \\ \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\underline{\upsilon } _{1}(\varkappa )\right\vert _{\varkappa = 0} = 0, \underline{\upsilon }_{1}(0) = 0, \underline{\upsilon }_{1}(b) = \sum_{i = 1}^{m}\delta _{i}I_{0^{+}}^{\sigma _{i}, \phi }\underline{\upsilon }_{1}(\zeta _{i}). \end{array} \right. \end{equation} (3.4)

    With reference to the definitions of the lower solution \underline{\upsilon }(\varkappa) = \underline{\upsilon }_{0}(\varkappa) and putting \Xi (\varkappa) = P_{1}(\varkappa)- \underline{ P } _{0}(\varkappa) , where P_{1}(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \upsilon _{1}(\varkappa)\ and \underline{P}_{0}(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{\upsilon }_{0}(\varkappa). Then, we get

    \begin{equation*} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \Xi \geq 0, \;\varkappa \in (0, b], \\ \Xi (0)\geq 0. \ \ \end{array} \right. \end{equation*}

    Consequently, Lemma 2.9 implies \Xi (\varkappa)\geq 0, that means \underline{ P } _{0}(\varkappa)\leq P_{1}(\varkappa), \varkappa \in \mathcal{J} and by the same technique, where P(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \upsilon (\varkappa) we get \upsilon (\varkappa)\geq 0. Hence, \underline{ \upsilon }_{0}(\varkappa)\leq \underline{\upsilon }_{1}(\varkappa), \varkappa \in \mathcal{J}. Now, from Eq (3.4) and our assumptions, we infer that

    \begin{equation*} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{ \upsilon }_{1}(\varkappa ) = f\left( \varkappa, \underline{\upsilon } _{0}(\varkappa )\right) \leq f\left( \varkappa, \underline{\upsilon } _{1}(\varkappa )\right). \end{equation*}

    Therefore, \underline{\upsilon }_{1} is a lower solution of problem (1.1). In the same way of the above argument, we conclude that \underline{ \upsilon }_{1}(\varkappa)\leq \underline{\upsilon }_{2}(\varkappa), \varkappa \in \mathcal{J}. By mathematical induction, we get \underline{ \upsilon }_{j}(\varkappa)\leq \underline{\upsilon }_{j+1}(\varkappa), \varkappa \in \mathcal{J}, j\geq 2.

    Similarly, we put \Xi (\varkappa) = \overline{P}_{1}(\varkappa)-\underline{P }_{1}(\varkappa) , where \overline{P}_{1}(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \overline{ \upsilon }_{1}(\varkappa)\ and \underline{P}_{1}(\varkappa) = \left(^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{ \upsilon }_{1}(\varkappa). Then, we get

    \begin{equation*} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \Xi (\varkappa )\geq 0, \;\varkappa \in (0, b], \\ \Xi (0)\geq 0. \ \ \end{array} \right. \end{equation*}

    Consequently, Lemma 2.9 implies \Xi (\varkappa)\geq 0, that means \overline{P}_{1}(\varkappa)\leq \underline{P}_{1}(\varkappa), \varkappa \in \mathcal{J} and by the same technique, we get \overline{\upsilon } _{1}(\varkappa)\geq \underline{\upsilon }_{1}(\varkappa), \varkappa \in \mathcal{J}. By mathematical induction, we get \overline{\upsilon } _{j}(\varkappa)\geq \underline{\upsilon }_{j}(\varkappa), \varkappa \in \mathcal{J}, j\geq 0.

    \textsf{Step (3):} In view of Eq (3.3), one can show that the sequences \left\{ \underline{\upsilon }_{j}\right\} _{j = 0}^{\infty } and \left\{ \overline{\upsilon }_{j}\right\} _{j = 0}^{\infty } are equicontinuous and uniformly bounded. In view of Arzela-Ascoli Theorem, we have \lim_{j\rightarrow \infty }\underline{\upsilon }_{j} = \upsilon _{\ast } and \lim_{j\rightarrow \infty }\overline{\upsilon }_{j} = \upsilon ^{\ast } uniformly on J and the limit of the solutions \upsilon _{\ast } and \upsilon ^{\ast } satisfy the problem (1.1). Moreover, \upsilon _{\ast } , \upsilon ^{\ast }\in \Phi .

    \textsf{Step (4):} We will prove that \upsilon _{\ast } and \upsilon ^{\ast } are the extremal solutions of the problem (1.1) in \Phi . For this end, let \upsilon \in \Phi be a solution of the problem (1.1) such that \overline{\upsilon }_{j}(\varkappa)\geq \upsilon (\varkappa)\geq \underline{\upsilon }_{j}(\varkappa), \varkappa \in \mathcal{J}, for some j\in \mathbb{N}. Therefore, by our assumption, we find that

    \begin{equation*} f\left( \varkappa, \overline{\upsilon }_{j}\left( \varkappa \right) \right) \geq f\left( \varkappa, \upsilon \left( \varkappa \right) \right) \geq f\left( \varkappa, \underline{\upsilon }_{j}\left( \varkappa \right) \right). \end{equation*}

    Hence

    \begin{eqnarray*} &&\left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \overline{\upsilon }_{j+1}(\varkappa ) \\ &&\left. \geq \right. \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \upsilon (\varkappa ) \\ &&\left. \geq \right. \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{\upsilon }_{j+1}(\varkappa ), \end{eqnarray*}

    and

    \begin{equation*} \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\overline{\upsilon } _{j+1}(\varkappa )\right\vert _{\varkappa = 0} = \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\upsilon (\varkappa )\right\vert _{\varkappa = 0} = \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\underline{\upsilon } _{j+1}(\varkappa )\right\vert _{\varkappa = 0} = 0. \end{equation*}

    Consequently, \overline{\upsilon }_{j+1}(\varkappa)\geq \upsilon (\varkappa)\geq \underline{\upsilon }_{j+1}(\varkappa), \varkappa \in \mathcal{J} . It follows that

    \begin{equation} \overline{\upsilon }_{j}(\varkappa )\geq \upsilon (\varkappa )\geq \underline{\upsilon }_{j}(\varkappa ), \varkappa \in \mathcal{J}, \ j\in \mathbb{N}. \end{equation} (3.5)

    Taking the limit of Eq (3.5) as j\rightarrow \infty , we get \upsilon ^{\ast }(\varkappa)\geq \upsilon (\varkappa)\geq \upsilon _{\ast }(\varkappa) , \varkappa \in \mathcal{J} . That is, \upsilon ^{\ast } and \upsilon _{\ast } are the extremal solutions of the problem (1.1) in \Phi .

    Corollary 3.5. Assume that f:\mathcal{J}\times \mathbb{R} ^{+}\rightarrow \mathbb{R} ^{+} is continuous, and there exist {\bm{\aleph}} _{1}, {\bm{\aleph}} _{2} > 0 such that

    \begin{equation} {\bm{\aleph}} _{1}\leq f\left( \varkappa, \upsilon \right) \leq {\bm{\aleph}} _{2}, \mathit{\mbox{}} \forall (\varkappa, \upsilon )\in \mathcal{J}\times \mathbb{R} ^{+}. \end{equation} (3.6)

    Then the problem (1.1) has at least one solution \upsilon (\varkappa)\in \mathcal{C}\left(\mathcal{J}\right). Moreover

    \begin{eqnarray} \upsilon (\varkappa ) &\leq &\frac{\left[ \phi (\varkappa )-\phi (0)\right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{2}\right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( \lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) {\bm{\aleph}}_{2}\right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{2}\right) \end{eqnarray} (3.7)

    and

    \begin{eqnarray} \upsilon (\varkappa ) &\geq &\frac{\left[ \phi (\varkappa )-\phi (0)\right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{1}\right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( \lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) {\bm{\aleph}}_{1}\right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{1}\right). \end{eqnarray} (3.8)

    Proof. From Eq (3.6) and definition of control functions, we get

    \begin{equation} {\bm{\aleph}}_{1}\leq \underline{f}\left( \varkappa, \upsilon (\varkappa )\right) \leq \overline{f}\left( \varkappa, \upsilon (\varkappa )\right) \leq {\bm{\aleph}}_{2}, \mbox{ }\forall (\varkappa, \upsilon )\in \mathcal{J}\times \mathbb{R} ^{+}. \end{equation} (3.9)

    Now, we consider the following problem

    \begin{equation} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \overline{ \upsilon }(\varkappa ) = {\bm{\aleph}}_{2}, \;\varkappa \in (0, b], \\ \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\overline{\upsilon } (\varkappa )\right\vert _{\varkappa = 0} = 0, \mbox{ }\overline{\upsilon }(0) = 0, \mbox{ }\overline{\upsilon }(b) = \sum_{i = 1}^{m}\delta _{i}I_{0^{+}}^{\sigma _{i}, \phi }\overline{\upsilon }(\zeta _{i}). \end{array} \right. \end{equation} (3.10)

    In view of Lemma 2.8, the problem (3.10) has a solution

    \begin{eqnarray*} \overline{\upsilon }(\varkappa ) & = &\frac{\left[ \phi (\varkappa )-\phi (0) \right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{2}\right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( \lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) {\bm{\aleph}}_{2}\right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{2}\right). \end{eqnarray*}

    Taking into account Eq (3.9), we obtain

    \begin{eqnarray*} \overline{\upsilon }(\varkappa ) &\geq &\frac{\left[ \phi (\varkappa )-\phi (0)\right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) \overline{f}\left( b, \overline{\upsilon }(b)\right) \right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( \lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) \overline{f}\left( \zeta _{i}, \overline{\upsilon } (\zeta _{i})\right) \right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) \overline{f}\left( \varkappa, \overline{\upsilon } (\varkappa )\right) \right). \end{eqnarray*}

    It is obvious that \overline{\upsilon }(\varkappa) is the upper solution of problem (1.1). Also, we consider the following problem

    \begin{equation} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \underline{ \upsilon }(\varkappa ) = {\bm{\aleph}}_{1}, \;\varkappa \in (0, b], \\ \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\underline{\upsilon } (\varkappa )\right\vert _{\varkappa = 0} = 0, \mbox{ }\underline{\upsilon }(0) = 0, \mbox{ }\underline{\upsilon }(b) = \sum_{i = 1}^{m}\delta _{i}I_{0^{+}}^{\sigma _{i}, \phi }\underline{\upsilon }(\zeta _{i}). \end{array} \right. \end{equation} (3.11)

    In view of Lemma 2.8, the problem (3.11) has a solution

    \begin{eqnarray*} \underline{\upsilon }(\varkappa ) & = &\frac{\left[ \phi (\varkappa )-\phi (0) \right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{1}\right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( \lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) {\bm{\aleph}}_{1}\right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) {\bm{\aleph}}_{1}\right). \end{eqnarray*}

    Taking into account Eq (3.9), we obtain

    \begin{eqnarray*} \underline{\upsilon }(\varkappa ) &\leq &\frac{\left[ \phi (\varkappa )-\phi (0)\right] ^{\gamma _{2}-1}E_{\mu _{2}, \gamma _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) }{\Theta } \\ &&\left[ \Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (b)-\phi (0)\right] ^{\mu _{2}}\right) \right. \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (b)-\phi (0)\right] ^{\mu _{1}}\right) \underline{f}\left( b, \underline{\upsilon }(b)\right) \right) \\ &&-\sum\limits_{i = 1}^{m}\delta _{i}\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}+\sigma _{i}, \phi }E_{\mu _{2}, \mu _{2}+\sigma _{i}}\left( \lambda _{2} \left[ \phi (\zeta _{i})-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left. \left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\zeta _{i})-\phi (0) \right] ^{\mu _{1}}\right) \underline{f}\left( \zeta _{i}, \underline{ \upsilon }(\zeta _{i})\right) \right) \right] \\ &&+\Gamma \left( \mu _{2}\right) I_{0^{+}}^{\mu _{2}, \phi }E_{\mu _{2}, \mu _{2}}\left( -\lambda _{2}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{2}}\right) \\ &&\left( \Gamma \left( \mu _{1}\right) I_{0^{+}}^{\mu _{1}, \phi }E_{\mu _{1}, \mu _{1}}\left( -\lambda _{1}\left[ \phi (\varkappa )-\phi (0)\right] ^{\mu _{1}}\right) \underline{f}\left( \varkappa, \underline{\upsilon } (\varkappa )\right) \right). \end{eqnarray*}

    Thus, \underline{\upsilon }(\varkappa) is the lower solution of problem (1.1).

    The application of Theorem 3.4 results that problem (1.1) has at least one solution \upsilon (\varkappa)\in \mathcal{C}\left(\mathcal{J} \right) that satisfies the inequalities (3.7) and (3.8).

    Example 4.1. Let us consider the following problem

    \begin{equation} \left\{ \begin{array}{c} \left( ^{H}D_{0^{+}}^{\mu _{1}, \beta _{1};\phi }+\lambda _{1}\right) \left( ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }+\lambda _{2}\right) \upsilon (\varkappa ) = f\left( \varkappa, \upsilon (\varkappa )\right) , \; \varkappa \in \lbrack 0, 1], \\ \left. ^{H}D_{0^{+}}^{\mu _{2}, \beta _{2};\phi }\upsilon (\varkappa )\right\vert _{\varkappa = 0} = 0, \upsilon (0) = 0, \upsilon (b) = \sum_{i = 1}^{m}\delta _{i}I_{0^{+}}^{\sigma _{i}, \phi }\upsilon (\zeta _{i}), \ \end{array} \right. \end{equation} (4.1)

    Here \mu _{1} = \frac{1}{2}, \mu _{2} = \frac{3}{2}, \beta _{1} = \beta _{2} = \frac{1 }{3}, \gamma _{1} = \frac{2}{3}, \gamma _{2} = \frac{4}{3}, \lambda _{1}\mathcal{ = } \lambda _{2} = 10, m = 1, \delta _{1} = \frac{1}{4}, \sigma _{1} = \frac{2}{3}, \zeta _{1} = \frac{3}{4}, b = 1 , \phi = e^{\varkappa }, \lambda _{1}\mathcal{ = }\lambda _{2} = 10 and we set f\left(\varkappa, \upsilon (\varkappa)\right) = 2+\varkappa ^{2}+\frac{\varkappa ^{3}}{5\left(1+\upsilon (\varkappa)\right) }\upsilon (\varkappa). For \upsilon, w\in\mathbb{R}^{+}, \varkappa \in \mathcal{J} , we have

    \begin{eqnarray*} \left\vert f\left( \varkappa, \upsilon \right) -f\left( \varkappa, w\right) \right\vert & = &\left\vert \left( 2+\varkappa ^{2}+\frac{\varkappa ^{3}}{ 5\left( 1+\upsilon (\varkappa )\right) }\upsilon (\varkappa )\right) -\left( 2+\varkappa ^{2}+\frac{\varkappa ^{3}}{5\left( 1+w(\varkappa )\right) } w(\varkappa )\right) \right\vert \\ &\leq &\frac{1}{5}\left\vert \upsilon (\varkappa )-w(\varkappa )\right\vert. \end{eqnarray*}

    By the given data, we get Q_{1}\approx 0.9 < 1 and hence all conditions in Theorem 3.3 are satisfied with \kappa = \frac{1}{5} > 0. Thus, the problem (4.1) has a unique solution \upsilon \in C\left(\mathcal{J }\right). On the other hand, from Theorem 3.4 and Theorem 3.3, the sequences \left\{ \underline{ \upsilon }_{n}\right\} _{n = 0}^{\infty } and \left\{ \overline{\upsilon } _{n}\right\} _{n = 0}^{\infty } can be obtained as

    \begin{eqnarray} \overline{\upsilon }_{n+1}(\varkappa ) & = &\Gamma \left( \frac{3}{2}\right) I_{0^{+}}^{\frac{3}{2}, e^{\varkappa }}E_{\frac{3}{2}, \frac{3}{2}}\left( 10 \left[ e^{\varkappa }-1\right] ^{\frac{3}{2}}\right) \\ &&\left( \Gamma \left( \frac{1}{2}\right) I_{0^{+}}^{\frac{1}{2}, e^{\varkappa }}E_{\frac{1}{2}, \frac{1}{2}}\left( 10\left[ e^{\varkappa }-1 \right] ^{\frac{1}{2}}\right) \left( 2+\varkappa ^{2}+\frac{1}{5\left( 1+ \overline{\upsilon }_{n}(\varkappa )\right) }\varkappa ^{3}\overline{ \upsilon }_{n}(\varkappa )\right) \right). \end{eqnarray} (4.2)

    and

    \begin{eqnarray} \underline{\upsilon }_{n+1}(\varkappa ) & = &\Gamma \left( \frac{3}{2}\right) I_{0^{+}}^{\frac{3}{2}, e^{\varkappa }}E_{\frac{3}{2}, \frac{3}{2}}\left( 10 \left[ e^{\varkappa }-1\right] ^{\frac{3}{2}}\right) \\ &&\left( \Gamma \left( \frac{1}{2}\right) I_{0^{+}}^{\frac{1}{2}, e^{\varkappa }}E_{\frac{1}{2}, \frac{1}{2}}\left( 10\left[ e^{\varkappa }-1 \right] ^{\frac{1}{2}}\right) \left( 2+\varkappa ^{2}+\frac{1}{5\left( 1+ \underline{\upsilon }_{n}(\varkappa )\right) }\varkappa ^{3}\underline{ \upsilon }_{n}(\varkappa )\right) \right). \end{eqnarray} (4.3)

    Moreover, for any \upsilon \in\mathbb{R}^{+} and \varkappa \in \left[0, 1\right] , we have

    \begin{eqnarray*} \lim\limits_{\upsilon \rightarrow +\infty }f\left( \varkappa, \upsilon (\varkappa )\right) & = &\lim\limits_{\upsilon \rightarrow +\infty }\left( 2+\varkappa ^{2}+ \frac{\varkappa ^{3}}{5\left( 1+\upsilon (\varkappa )\right) }\upsilon (\varkappa )\right) \\ & = &2+\varkappa ^{2}+\frac{\varkappa ^{3}}{5}. \end{eqnarray*}

    It follows that

    \begin{equation*} 2 < f\left( \varkappa, \upsilon (\varkappa )\right) < \frac{16}{5}. \end{equation*}

    Thus, by Corollary 3.5, we get {\bm{\aleph}}_{1} = 2 and {\bm{\aleph}}_{2} = \frac{16}{5}. Then by Definitions 3.1 and 3.2, the problem (4.1) has a solution which verifies \underline{ \upsilon } \left(\varkappa \right) \leq \upsilon \left(\varkappa \right) \leq \overline{ \upsilon }\left(\varkappa \right) where

    \begin{eqnarray} \overline{\upsilon }(\varkappa ) & = &\frac{\left( e^{\varkappa }-1\right) ^{ \frac{4}{3}-1}E_{\frac{3}{2}, \frac{4}{3}}\left( -10\left( e^{\varkappa }-1\right) ^{\frac{3}{2}}\right) }{\Theta } \\ &&2\left[ \Gamma \left( \frac{3}{2}\right) \Gamma \left( \frac{1}{2}\right) \left( e-1\right) ^{2}E_{\frac{3}{2}, 3}\left( -10\left( e-1\right) ^{\frac{3 }{2}}\right) E_{\frac{1}{2}, 1}\left( -10\left( e-1\right) ^{\frac{1}{2} }\right) \right. \\ &&\left. -\frac{4}{5}\Gamma \left( \frac{3}{2}\right) \Gamma \left( \frac{1}{ 2}\right) \left( e^{\frac{3}{4}}-1\right) ^{\frac{7}{3}}E_{\frac{3}{2}, \frac{ 21}{6}}\left( -10\left( e^{\frac{3}{4}}-1\right) ^{\frac{3}{2}}\right) E_{ \frac{1}{2}, 1}\left( -10\left( e^{\frac{3}{4}}-1\right) ^{\frac{1}{2} }\right) \right] \\ &&+\frac{16}{5}\Gamma \left( \frac{3}{2}\right) \Gamma \left( \frac{1}{2} \right) \left( e^{\varkappa }-1\right) ^{2}E_{\frac{3}{2}, 3}\left( -10\left( e-1\right) ^{\frac{3}{2}}\right) E_{\frac{1}{2}, 1}\left( -10\left( e^{\varkappa }-1\right) ^{\frac{1}{2}}\right), \end{eqnarray} (4.4)

    and

    \begin{eqnarray} \underline{\upsilon }(\varkappa ) & = &\frac{\left( e^{\varkappa }-1\right) ^{ \frac{4}{3}-1}E_{\frac{3}{2}, \frac{4}{3}}\left( -10\left( e^{\varkappa }-1\right) ^{\frac{3}{2}}\right) }{\Theta } \\ &&\frac{16}{5}\left[ \Gamma \left( \frac{3}{2}\right) \Gamma \left( \frac{1}{ 2}\right) \left( e-1\right) ^{2}E_{\frac{3}{2}, 3}\left( -10\left( e-1\right) ^{\frac{3}{2}}\right) E_{\frac{1}{2}, 1}\left( -10\left( e-1\right) ^{\frac{1 }{2}}\right) \right. \\ &&\left. -\frac{1}{2}\Gamma \left( \frac{3}{2}\right) \Gamma \left( \frac{1}{ 2}\right) \left( e^{\frac{3}{4}}-1\right) ^{\frac{7}{3}}E_{\frac{3}{2}, \frac{ 21}{6}}\left( -10\left( e^{\frac{3}{4}}-1\right) ^{\frac{3}{2}}\right) E_{ \frac{1}{2}, 1}\left( -10\left( e^{\frac{3}{4}}-1\right) ^{\frac{1}{2} }\right) \right] \\ &&+2\Gamma \left( \frac{3}{2}\right) \Gamma \left( \frac{1}{2}\right) \left( e^{\varkappa }-1\right) ^{2}E_{\frac{3}{2}, 3}\left( -10\left( e-1\right) ^{ \frac{3}{2}}\right) E_{\frac{1}{2}, 1}\left( -10\left( e^{\varkappa }-1\right) ^{\frac{1}{2}}\right), \end{eqnarray} (4.5)

    are respectively the upper and lower solutions of the problem (4.1) and

    \begin{equation*} \Theta : = \left( \frac{1}{4}\left[ e^{\frac{3}{4}}-1\right] ^{1}E_{\frac{3}{2}, 2}\left( -10\left( e^{\frac{3}{4}}-1\right) ^{\frac{3}{2}}\right) -\left[ e-1\right] ^{\frac{4}{3}-1}E_{\frac{3}{2}, \frac{4}{3}}\left( -10\left( e-1\right) ^{\frac{3}{2}}\right) \right) \neq 0. \end{equation*}

    Let us see graphically, we plot in Figure 1 the behavior of the upper solution \overline{\upsilon} and lower solution \underline{\upsilon} of the problem (4.1) with given data above.

    Figure 1.  Graphical presentation of (\underline{ \upsilon}, \overline{\upsilon}) .

    In this work, we have proved successfully the monotone iterative method is an effective method to study FLEs in the frame of \phi -Hilfer fractional derivative with multi-point boundary conditions. Firstly, the formula of explicit solution of \phi -Hilfer type FLE (1.1) in the term of Mittag-Leffler function has been derived. Next, we have investigated the lower and upper explicit monotone iterative sequences and proved that converge to the extremal solution of boundary value problems with multi-point boundary conditions. Finally, a numerical example has been given in order to illustrate the validity of our results.

    Furthermore, it will be very important to study the present problem in this article regarding the Mittag-Leffler power low [36], the generalized Mittag-Leffler power low with another function [37,38], and the fractal-fractional operators [39].

    Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project. The authors are also grateful to the anonymous referees for suggestions that have improved manuscript.

    The authors declare that they have no competing interests.



    [1] Z. Chen, J. Zou, Finite element methods and their convergence for elliptic and parabolic interface problems, Numer. Math., 79 (1998), 175–202. https://doi.org/10.1007/s002110050336 doi: 10.1007/s002110050336
    [2] J. Huang, J. Zou, Uniform a priori estimates for elliptic and static Maxwell interface problems, Discrete Contin. Dyn. Syst. Ser. B, 7 (2007), 145–170. https://doi.org/10.3934/dcdsb.2007.7.145 doi: 10.3934/dcdsb.2007.7.145
    [3] D. Braess, Finite elements: theory, fast solver, and applications in solid mechanics, 3 Eds., Cambridge University Press, UK, 2007.
    [4] P. Zhu, Q. Zhang, BDF Schemes in stable generalized finite element methods for parabolic interface problems with moving interfaces, CMES-Comput. Model. Eng. Sci., 124 (2020), 107–127. https://doi.org/10.32604/cmes.2020.09831 doi: 10.32604/cmes.2020.09831
    [5] J. W. Barrett, C. M. Elliott, Fitted and unfitted finite-element methods for elliptic equations with smooth interfaces, IMA J. Numer. Anal., 7 (1987), 283–300. https://doi.org/10.1093/imanum/7.3.283 doi: 10.1093/imanum/7.3.283
    [6] Z. Li, T. Lin, X. Wu, New Cartesian grid methods for interface problems using the finite element formulation, Numer. Math., 96 (2003), 61–98. https://doi.org/10.1007/s00211-003-0473-x doi: 10.1007/s00211-003-0473-x
    [7] S. Adjerid, T. Lin, H. Meghaichi, A high order geometry conforming immersed finite element for elliptic interface problems, Comput. Methods Appl. Mech. Engrg., 420 (2024), 116703. https://doi.org/10.1016/j.cma.2023.116703 doi: 10.1016/j.cma.2023.116703
    [8] R. Guo, T. Lin, X. Zhang, Nonconforming immersed finite element spaces for elliptic interface problems, Comput. Math. Appl., 75 (2018), 2002–2016. https://doi.org/10.1016/j.camwa.2017.10.040 doi: 10.1016/j.camwa.2017.10.040
    [9] T. Lin, Y. Lin, X. Zhang, Partially penalized immersed finite element methods for elliptic interface problems, SIAM J. Numer Anal., 53 (2015), 1121–1144. https://doi.org/10.1137/130912700 doi: 10.1137/130912700
    [10] R. E. Ewing, Z. Li, T. Lin, Y. Lin, The immersed finite volume element methods for the elliptic interface problems, Math. Comput. Simul., 50 (1999), 63–76. https://doi.org/10.1016/S0378-4754(99)00061-0 doi: 10.1016/S0378-4754(99)00061-0
    [11] L. Zhu, Z. Zhang, Z. Li, An immersed finite volume element method for 2D PDEs with discontinuous coefficients and non-homogeneous jump conditions, Comput. Math. Appl., 70 (2015), 89–103. https://doi.org/10.1016/j.camwa.2015.04.012 doi: 10.1016/j.camwa.2015.04.012
    [12] Q. Wang, J. Xie, Z. Zhang, L. Wang, Bilinear immersed finite volume element method for solving matrix coefficient elliptic interface problems with non-homogeneous jump conditions, Comput. Math. Appl., 86 (2021), 1–15. https://doi.org/10.1016/j.camwa.2020.12.016 doi: 10.1016/j.camwa.2020.12.016
    [13] Q. Wang, Z. Zhang. A stabilized immersed finite volume element method for elliptic interface problems, Appl. Numer. Math., 143 (2019), 75–87. https://doi.org/10.1016/j.apnum.2019.03.010 doi: 10.1016/j.apnum.2019.03.010
    [14] Q. Wang, Z. Zhang, L. Wang, New immersed finite volume element method for elliptic interface problems with non-homogeneous jump conditions, J. Comput. Phys., 427 (2021), 110075. https://doi.org/10.1016/j.jcp.2020.110075 doi: 10.1016/j.jcp.2020.110075
    [15] T. Strouboulis, K. Copps, I. Babuška, The generalized finite element method, Comput. Methods Appl. Mech. Engrg., 190 (2001), 4081–4193. https://doi.org/10.1016/S0045-7825(01)00188-8 doi: 10.1016/S0045-7825(01)00188-8
    [16] I. Babuška, U. Banerjee, J. E. Osborn, Generalized finite element methods-mail ideas, results and perspective, Int. J. Comput. Methods, 1 (2004), 67–103. https://doi.org/10.1142/S0219876204000083 doi: 10.1142/S0219876204000083
    [17] T. Belytschko, R. Gracie, G. Ventura, A review of extended/generalized finite element methods for material modeling, Model. Simul. Mater. Sci Eng., 17 (2009), 043001. https://doi.org/10.1088/0965-0393/17/4/043001 doi: 10.1088/0965-0393/17/4/043001
    [18] T. P. Fries, T. Belytschko, The extended/generalized finite element method: an overview of the method and its applications, Int. J. Numer. Methods Eng., 84 (2010), 253–304. https://doi.org/10.1002/nme.2914 doi: 10.1002/nme.2914
    [19] K. W. Cheng, T. P. Fries, Higher-order XFEM for curved strong and weak discontinuities, Internat. J. Numer. Methods Engrg., 82 (2010), 564–590. https://doi.org/10.1002/nme.2768 doi: 10.1002/nme.2768
    [20] H. Sauerland, T. P. Fries, The extended finite element method for two-phase and free-surface flows: a systematic study, J. Comput. Phys., 230 (2011) 3369–3390. https://doi.org/10.1016/j.jcp.2011.01.033 doi: 10.1016/j.jcp.2011.01.033
    [21] I. Babuška, U. Banerjee, Stable generalized finite element method, Comput. Methods Appl. Mech. Engrg., 201–204 (2012), 91–111. https://doi.org/10.1016/j.cma.2011.09.012 doi: 10.1016/j.cma.2011.09.012
    [22] K. Kergrene, I. Babuška, U. Banerjee, Stable generalized finite element method and associated iterative schemes: application to interface problems, Comput. Methods Appl. Mech. Engrg., 305 (2016), 1–36. https://doi.org/10.1016/j.cma.2016.02.030 doi: 10.1016/j.cma.2016.02.030
    [23] I. Babuška, U. Banerjee, K. Kergrene, Strongly stable generalized finite element method: application to interface problems, Comput. Methods Appl. Mech. Engrg., 327 (2017), 58–92. https://doi.org/10.1016/j.cma.2017.08.008 doi: 10.1016/j.cma.2017.08.008
    [24] Q. Zhang, U. Banerjee, I. Babuška, High order stable generalized finite element methods, Numer. Math., 128 (2014), 1–29. https://doi.org/10.1007/s00211-014-0609-1 doi: 10.1007/s00211-014-0609-1
    [25] Q. Zhang, I. Babuška, A stable generalized finite element method (SGFEM) of degree two for interface problems, Comput. Methods Appl. Mech. Engrg., 363 (2020), 112889. https://doi.org/10.1016/j.cma.2020.112889 doi: 10.1016/j.cma.2020.112889
    [26] Q. Deng, V. Calo, Higher order stable generalized finite element method for the elliptic eigenvalue and source problems with an interface in 1D, J. Comput. Appl. Math., 368 (2020), 112558. https://doi.org/10.1016/j.cam.2019.112558 doi: 10.1016/j.cam.2019.112558
    [27] Q. Zhang, U. Banerjee, I. Babuška, Strongly stable generalized finite element method (SSGFEM) for a non-smooth interface problem, Comput. Methods Appl. Mech. Engrg., 344 (2019), 538–568. https://doi.org/10.1016/j.cma.2018.10.018 doi: 10.1016/j.cma.2018.10.018
    [28] Q. Zhang, U. Banerjee, I. Babuška, Strongly stable generalized finite element method (SSGFEM) for a non-smooth interface problem Ⅱ: a simplified algorithm, Comput. Methods Appl. Mech. Engrg., 363 (2020), 112926. https://doi.org/10.1016/j.cma.2020.112926 doi: 10.1016/j.cma.2020.112926
    [29] W. Gong, H. Li, Q. Zhang, Improved enrichments and numerical integrations in SGFEM for interface problems, J. Comput. Appl. Math., 438 (2024), 115540. https://doi.org/10.1016/j.cam.2023.115540 doi: 10.1016/j.cam.2023.115540
    [30] Q. Zhang, I. Babuška, U. Banerjee, Robustness in stable generalized finite element methods (SGFEM) applied to Poisson problems with crack singularities, Comput. Methods Appl. Mech. Engrg., 311 (2016), 476–502. https://doi.org/10.1016/j.cma.2016.08.019 doi: 10.1016/j.cma.2016.08.019
    [31] H. Li, C. Cui, Q. Zhang, Stable generalized finite element methods (SGFEM) for interfacial crack problems in bi-materials, Eng. Anal. Bound. Elem., 138 (2022), 83–94. https://doi.org/10.1016/j.enganabound.2022.01.010 doi: 10.1016/j.enganabound.2022.01.010
    [32] P. Zhu, Q. Zhang, T. Liu, Stable generalized finite element method (SGFEM) for parabolic interface problems, J. Comput. Appl. Math., 367 (2020), 112475. https://doi.org/10.1016/j.cam.2019.112475 doi: 10.1016/j.cam.2019.112475
    [33] V. Gupta, C. A. Duarte, I. Babuška, U. Banerjee, A stable and optimally convergent generalized FEM (SGFEM) for linear elastic fracture mechanics, Comput. Methods Appl. Mech. Engrg., 266 (2013), 23–39. https://doi.org/10.1016/j.cma.2013.07.010 doi: 10.1016/j.cma.2013.07.010
    [34] A. G. Sanchez-Rivadeneira, C. A. Duarte, A stable generalized/extended FEM with discontinuous interpolants for fracture mechanics, Comput. Methods Appl. Mech. Engrg., 345 (2019), 876–918. https://doi.org/10.1016/j.cma.2018.11.018 doi: 10.1016/j.cma.2018.11.018
    [35] A. G. Sanchez-Rivadeneira, N. Shauer, B. Mazurowski, C. A. Duarte, A stable generalized/extended p-hierarchical FEM for three-dimensional linear elastic fracture mechanics, Comput. Methods Appl. Mech. Engrg., 364 (2020), 112970. https://doi.org/10.1016/j.cma.2020.112970 doi: 10.1016/j.cma.2020.112970
    [36] N. Moës, M. Cloirec, P. Cartraud, J. F. Remacle, A computational approach to handle complex microstructure geometries, Comput. Methods Appl. Mech. Engrg., 192 (2003), 3163–3177. https://doi.org/10.1016/S0045-7825(03)00346-3 doi: 10.1016/S0045-7825(03)00346-3
    [37] Q. Zhang, C. Cu, U. Banerjee, I. Babuška, A condensed generalized finite element methods (CGFEM) for interface problems, Comput. Methods Appl. Mech. Engrg., 391 (2022), 114537. https://doi.org/10.1016/j.cma.2021.114537 doi: 10.1016/j.cma.2021.114537
    [38] G. Jo, D. Y. Kwak, Y. J. Lee, Locally conservative immersed finite element method for elliptic interface problems, J. Sci. Comput., 87 (2021), 60. https://doi.org/10.1007/s10915-021-01476-1 doi: 10.1007/s10915-021-01476-1
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