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Research article Topical Sections

Satisfaction of telehealth implementation in a pediatric feeding clinic

  • Objectives 

    Telehealth services became commonplace during the COVID-19 pandemic and were widely reported to improve access to medical care in a variety of settings. The primary aim of this study was to assess patient- and provider-reported satisfaction with telehealth services within a multidisciplinary outpatient program for children with feeding disorders.

    Methods 

    Caregivers and healthcare providers who participated in telehealth multidisciplinary visits within an outpatient pediatric feeding disorders clinic between April and June 2020 completed an online survey that assessed their visit satisfaction. The visit completion rates of in-person 2019 and virtual 2020 visits were compared.

    Results 

    Thirty-six caregivers of children between 1-month and 8-years-old completed the survey. Caregivers indicated their overall satisfaction with telehealth services, finding it more convenient than seeing specialists in person. Caregivers demonstrated interest in continuing telehealth visits. Providers indicated being satisfied with the telehealth visits, with many noting that they were as effective as in-person visits. There was an increase in the number of in-person visits between 2019 compared to virtual visits in 2020, though there were no differences for the visit completion rates.

    Conclusions 

    Both caregivers and providers were satisfied with the telehealth services and highlighted various benefits in response to open-ended questions. However, there were concerns with the lack of available anthropometric data and measurements. Although there were no differences in the no-show rates following the implementation of telehealth, there was a significant increase in the total number of completed visits. Telehealth visits are a crucial resource for caregivers and providers in multidisciplinary pediatric feeding clinics, yet enhancing anthropometric measurements is necessary to provide quality care.

    Citation: Ryan D. Davidson, Rebecca Kramer, Sarah Fleet. Satisfaction of telehealth implementation in a pediatric feeding clinic[J]. AIMS Medical Science, 2024, 11(2): 124-136. doi: 10.3934/medsci.2024011

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  • Objectives 

    Telehealth services became commonplace during the COVID-19 pandemic and were widely reported to improve access to medical care in a variety of settings. The primary aim of this study was to assess patient- and provider-reported satisfaction with telehealth services within a multidisciplinary outpatient program for children with feeding disorders.

    Methods 

    Caregivers and healthcare providers who participated in telehealth multidisciplinary visits within an outpatient pediatric feeding disorders clinic between April and June 2020 completed an online survey that assessed their visit satisfaction. The visit completion rates of in-person 2019 and virtual 2020 visits were compared.

    Results 

    Thirty-six caregivers of children between 1-month and 8-years-old completed the survey. Caregivers indicated their overall satisfaction with telehealth services, finding it more convenient than seeing specialists in person. Caregivers demonstrated interest in continuing telehealth visits. Providers indicated being satisfied with the telehealth visits, with many noting that they were as effective as in-person visits. There was an increase in the number of in-person visits between 2019 compared to virtual visits in 2020, though there were no differences for the visit completion rates.

    Conclusions 

    Both caregivers and providers were satisfied with the telehealth services and highlighted various benefits in response to open-ended questions. However, there were concerns with the lack of available anthropometric data and measurements. Although there were no differences in the no-show rates following the implementation of telehealth, there was a significant increase in the total number of completed visits. Telehealth visits are a crucial resource for caregivers and providers in multidisciplinary pediatric feeding clinics, yet enhancing anthropometric measurements is necessary to provide quality care.


    Abbreviations

    COVID-19:

    Coronavirus disease 2019; 

    MD:

    Doctor of medicine; 

    NP:

    Nurse practitioner; 

    CCC-SLP:

    Certificate of clinical competence in speech-language pathology; 

    CLC:

    Certified lactation counselor; 

    PhD:

    Doctor of philosophy; 

    REDCap:

    Research Electronic Data Capture; 

    SLP:

    Speech Language Pathologist; 

    M:

    Mean; 

    SD:

    Standard deviation

    The Sobolev equation plays an important role in partial differential equations (PDEs) because of its significant physical background, such as consolidation of clay [1], thermodynamics [2] and flow of fluids through fissured rock [3].

    In this article, a Legendre-tau-Galerkin method in time and its multi-interval form will be considered for the following 2D Sobolev equations:

    {tu(x,y,t)εtΔu(x,y,t)μΔu(x,y,t)+γu(x,y,t)=f(x,y,t),(x,y,t)Σ:=Ω×I,u(x,y,1)=u0(x,y),(x,y)ˉΩ,u(x,y,t)=0,(x,y,t)Ω×I, (1.1)

    where coefficients μ, ε, γ are known positive parameters. Though the the time interval is normally (0, T] and T>0, we set the interval as I=(1,1] and the spatial domain is Ω=(1,1)×(1,1). We consider the time interval I=(1,1] just to simplify the presentation of the theoretical analysis and the algorithm implementation process.

    Because of the great difficulty in obtaining analytical solutions of PDEs, various numerical methods [4,5,6,7] have been proposed to approximate the exact solutions. For the Sobolev equations, there have been many studies investigating the numerical solutions. In [8,9,10], some finite volume element methods were presented in space to solve the two-dimensional Sobolev equations combined with the finite difference schemes in time. The continuous interior penalty finite element method, space-time continuous Galerkin method and finite difference streamline diffusion method were applied in [11,12,13] for solving Sobolev equations with convection-dominated term, respectively. In [14], discontinuous Galerkin scheme in space and Crank-Nicolson scheme in time were considered for approaching the exact solutions of generalized Sobolev equations. In [15], Shi and Sun studied an H1-Galerkin mixed finite element method for solving Sobolev equations and presented the existence, uniqueness and superconvergence results of the discrete scheme. In [16,17], a block-centered finite difference scheme and a time discontinuous Galerkin space-time finite element scheme for nonlinear Sobolev equations were established respectively, and stability and global convergence of the schemes were strictly proved. In [18], the Legendre spectral element method in space combined with the Crank-Nicolson finite difference technique in time were considered. In [19], the nonlinear periodic Sobolev equations were investigated by the Fourier spectral method.

    As is well known, the spectral method is distinguished from other numerical methods by its exponential convergence, and when the spectral method is applied to time-dependent partial differential equations in both space and time (namely, space-time spectral method [20,21,22,23,24,25]), the mismatched accuracy caused by the spectral discretization in space and the finite difference method in time can be solved successfully. In [26], we constructed a space-time Legendre spectral scheme for the linear multi-dimensional Sobolev equations for the first time and the exponential convergence was obtained in both space and time. The main purpose of this paper is to study the multi-interval form of the Legendre space-time fully discrete scheme of two-dimensional Sobolev equations by dividing the time interval. It is worth noting that compared with the single interval method, the multi-interval spectral method [27,28,29] can adopt parallel computation, reduce the scale of the problem effectively and improve the flexibility of the algorithm. Considering the asymmetry of the first order differential operator, the fully discrete scheme is constructed by applying a Legendre-tau-Galerkin method in time based on the Legendre Galerkin method in space. In addition, we still apply the Fourier-like basis functions [30] in space to diagonalize the stiff matrix and the mass matrix simultaneously, which greatly saves the computing time and memory.

    The organization of this article is as follows. In Section 2, we first provide some related notations, then establish the single interval Legendre space-time spectral fully discrete scheme of Eq (1.1) and give the stability analysis and L2(Σ)-error estimates. In Section 3, we divide the time interval and develop the multi-interval Legendre space-time spectral fully discrete scheme of the equations, and then strictly prove the L2(Σ)-error estimates. In Section 4, by using Fourier-like basis functions in space and selecting appropriate basis functions in time, we present the implementation of the multi-interval fully discrete scheme. In Section 5, numerical tests are included to access the efficiency and accuracy of the method. Finally, some conclusions are made in Section 6.

    Throughout the paper, the Sobolev spaces in spatial directions are the standard notations used, namely,

    v(x,y)r,Ω=(|ϵ|rDϵv(x,y)2Ω)12,v(x,y)Hr(Ω), (2.1)

    where ϵ=(ϵ1,ϵ2)(ϵi0 are integers and |ϵ|=ϵ1+ϵ2), Dϵv(x,y)=|ϵ|vxϵ1yϵ2 and r,Ω is denoted by Ω when r=0.

    The temporal direction involves a weighted Sobolev space L2ωα,β(I) endowed with the norm and product

    v(t)2I,ωα,β=(v(t),v(t))I,ωα,β=Iv2ωα,βdt,v(t)L2ωα,β(I), (2.2)

    where the weight function is ωα,β(t)=(1t)α(1+t)β. If α=β=0, the norm I and inner product (,)I are denoted in the space L2(I).

    Furthermore, the weighted space-time Sobolev space L2ωα,β(I;Hr(Ω)) is endowed with the norm

    v(x,y,t)L2ωα,β(I;Hr(Ω))=(Iv(x,y,t)2r,Ωωα,βdt)12,v(x,y,t)L2ωα,β(I;Hr(Ω)), (2.3)

    if r=0, the norm L2ωα,β(I;Hr(Ω)) is denoted by L2ωα,β(I;L2(Ω)); if α=β=0, the norm L2ωα,β(I;Hr(Ω)) is denoted by L2(I;Hr(Ω)); if r=0 and α=β=0, the norm L2ωα,β(I;Hr(Ω)) is denoted by L2(I;L2(Ω))=Σ.

    Let Pι be a space of polynomials of degree ι on [1,1] and L=(M,N), where M and N are a pair of given positive integers. In order to develop the single interval Legendre spectral method in time, we define

    V0N={vPN:v(±1)=0},V0N=V0NV0N,VM={vPM:v(1)=0}. (2.4)

    Then applying the Green's formula, we obtain the following single interval Legendre space-time fully discrete scheme of (1.1): Find uLV0NPM(I) satisfying

    {(tuL,v)Σ+ε(tuL,v)Σ+μ(uL,v)Σ+γ(uL,v)Σ=(f,v)Σ,vV0NVM,uL(x,y,1)=P1Nu0(x,y), (2.5)

    where P1N denote the spatial projection operator and its definition will be given below.

    Firstly, we introduce the definition of spatial projection operator and the corresponding lemma. Next, we present the existence, uniqueness and stability conclusion for the solution of (2.5).

    Definition 2.1. [31] Denote H10(Ω)={vH1(Ω):v|Ω=0}, then the orthogonal projection in space P1N:H10(Ω)V0N is given by

    ((P1Nuu),v)Ω=0,vV0N. (2.6)

    Lemma 2.1. [31] If vH10(Ω)Hr(Ω) and r1, we have

    NP1NvvΩ+(P1Nvv)ΩCN1rvr,Ω. (2.7)

    Theorem 2.1. Assume that u0(x,y)H10(Ω)Hr(Ω)(r1) and fL2(Σ), then the scheme (2.5) has a unique solution uL satisfying

    tuLΣ,ω1,0+uLΣ+uLΣC(u01,Ω+fΣ). (2.8)

    Proof. Taking v=(1t)tuL(V0NVM) and using the integration by parts, we can get for the left-hand side of (2.5)

    (tuL,(1t)tuL)Σ=tuL2Σ,ω1,0, (2.9)
    ε(tuL,(1t)tuL)Σ=ε(tuL,(1t)tuL)Σ=εtuL2Σ,ω1,0, (2.10)
    γ(uL,(1t)tuL)Σ=2γuL(1)2Ωγ(uL,(1t)tuL)Σ+γ(uL,uL)Σ, (2.11)

    namely,

    γ(uL,(1t)tuL)Σ=γuL(1)2Ω+γ2uL2Σ, (2.12)

    similarly,

    μ(uL,(1t)tuL)Σ=μuL(1)2Ω+μ2uL2Σ. (2.13)

    Additionally, by the Cauchy-Schwarz inequality and Young's inequality, the right-hand side of (2.5) can be estimated as

    (f,(1t)tuL)ΣfΣ(1t)tuLΣ12f2Σ+tuL2Σ,ω1,0. (2.14)

    Collecting (2.9)–(2.14) leads to

    tuL2Σ,ω1,0+εtuL2Σ,ω1,0+μ2uL2Σ+γ2uL2ΣγuL(1)2Ω+μuL(1)2Ω+12f2Σ+tuL2Σ,ω1,0, (2.15)

    namely,

    tuLΣ,ω1,0+uLΣ+uLΣC(uL(1)Ω+uL(1)Ω+fΣ). (2.16)

    For initial conditions uL(1) and uL(1) in (2.16), according to Lemma 2.1, we can easily get the following estimate:

    uL(1)Ω+uL(1)Ω=P1Nu0Ω+P1Nu0ΩP1Nu0u0Ω+u0Ω+(P1Nu0u0)Ω+u0ΩCNru0r,Ω+u0Ω+CN1ru0r,Ω+u0ΩCu0r,Ω. (2.17)

    Thus, combining estimations (2.16) and (2.17), we immediately attain the stability conclusion.

    Remark 2.1. From stability conclusion, there exists a zero solution if f=0 and u0=0. In other words, we can easily obtain the existence and uniqueness of uL.

    The purpose of this section is to show an L2(Σ)-error estimate of the single interval Legendre space-time spectral method by applying the dual technique. Now, we first introduce following definition and lemma of the time projection operator which will be covered later..

    Definition 2.2. [28] The orthogonal projection in time ΠM:H1(I)PM(I) is given by

    (ΠMuu,v)I=0,vVM, (2.18)

    and ΠMu(1)=u(1).

    Lemma 2.2. [28] (a) If uHσ(I) and σ1, then

    ΠMuuI,ωl,1CM14(1l)σσtuI,ωσ1,σ1,l=0,1. (2.19)

    (b) If uHσ(I) and σ2, then

    ΠMuuI,ω0,1CM18σσtuI,ωσ2,σ2. (2.20)

    Let U=P1NΠMu. Now we decompose the error into: uLu=(uLU)+(Uu) and denote ˜u=uLU. So according to (2.5) we have

    (t˜u,v)Σ+ε(t˜u,v)Σ+μ(˜u,v)Σ+γ(˜u,v)Σ=(t(uU),v)Σ+ε(t(uU),v)Σ+μ((uU),v)Σ+γ(uU,v)Σ,vV0NVM. (2.21)

    According to the Definitions 2.1 and 2.2, for the right-hand side terms of (2.21) we get

    ε(t(uU),v)Σ=ε(t(uΠMu),v)Σ+ε(t(ΠMuP1NΠMu),v)Σ=ε(t(uΠMu),v)Σ, (2.22)
    μ((uU),v)Σ=μ((uP1Nu),v)Σ+μ((P1NuP1NΠMu),v)Σ=0, (2.23)
    γ(uU,v)Σ=γ(uP1Nu,v)Σ+γ(P1NuP1NΠMu,v)Σ=γ(uP1Nu,v)Σ. (2.24)

    Then (2.21) can be simplified as follows:

    (t˜u,v)Σ+ε(t˜u,v)Σ+μ(˜u,v)Σ+γ(˜u,v)Σ=(t(uU),v)Σ+ε(t(IΠM)u,v)Σ+γ((IP1N)u,v)Σ. (2.25)

    According to the definition of ˜u and ΠM, we note

    ˜u(x,y,1)=uL(x,y,1)P1NΠMu(x,y,1)=P1Nu0(x,y)P1Nu(x,y,1)=0. (2.26)

    Now, we give the L2(Σ)-error estimates of the single interval Legendre space-time spectral scheme.

    Theorem 2.2. Suppose uL and u are the solutions of the scheme (2.5) and problem (1.1), respectively. If u0Hr(Ω)H10(Ω) and uHσ(I;Hr(Ω)H10(Ω)) for integers r1, then:

    (a) For σ1,

    uuLΣC{Nr(uL2(I;Hr(Ω))+uu0L2ω0,1(I;Hr(Ω)))+M14σ(σtuL2ωσ1,σ1(I;L2(Ω))+NrσtuL2ωσ1,σ1(I;Hr(Ω))+σtuL2ωσ1,σ1(I;L2(Ω)))}. (2.27)

    (b) For σ2,

    uuLΣC{Nr(uL2(I;Hr(Ω))+uu0L2ω0,1(I;Hr(Ω)))+M18σ(σtuL2ωσ2,σ2(I;L2(Ω))+NrσtuL2ωσ2,σ2(I;Hr(Ω))+σtuL2ωσ2,σ2(I;L2(Ω)))}. (2.28)

    Proof. In order to use the dual technique to attain the L2(Σ)-error estimates, denote H(I)={vH1(I):v(1)=0}. Then for Eq (2.25) we can write its dual equation: For a given gV0NPM(I), obtain ugV0NVM such that

    (te,ug)Σ+ε(te,ug)Σ+μ(e,ug)Σ+γ(e,ug)Σ=(g,e)Σ,eV0N(PM(I)H(I)). (2.29)

    Firstly, we present the existence and uniqueness of ug. Assuming g=0 and taking e=1t[ω1,0ug]t111sugds in (2.29), then we have

    (te,ug)Σ=(t1t[ω1,0ug],ug)Σ=(ω1,0ug,ug)Σ=ug2Σ,ω1,0, (2.30)
    ε(te,ug)Σ=ε(t1t[ω1,0ug],ug)Σ=ε(t1t[ω1,0ug],ug)Σ=ε(ω1,0ug,ug)Σ=εug2Σ,ω1,0, (2.31)
    μ(e,ug)Σ=μ(1t[ω1,0ug],ug)Σ=μ(φ,(1t)φt)Σ=μΩ((1t)φ2|11Iφt((1t)φ)dt)dxdy=μ(φ,t((1t)φ))Σ=μ(φ,(1t)φt))Σ+μ(φ,φ)Σ, (2.32)

    namely,

    μ(e,ug)Σ=μ21t[ω1,0ug]2Σ, (2.33)

    where φ=1t[ω0,1ug] and similarly

    γ(1t[ω1,0ug],ug)Σ=γ21t[ω1,0ug]2Σ. (2.34)

    Collecting (2.30)–(2.34), we have

    ug2Σ,ω1,0+εug2Σ,ω1,0+μ21t[ω1,0ug]2Σ+γ21t[ω1,0ug]2Σ=0. (2.35)

    Then ug=0.

    Now, in order to derive the estimate of ˜u, we first consider the estimates of ug, tug, tug.

    Taking e=(1+t)tug in (2.29), we can get

    (te,ug)Σ=(t((1+t)tug),ug)Σ=Ω((1+t)ugtug)|11dxdy+((1+t)tug,tug)Σ=tug2Σ,ω0,1, (2.36)
    ε(te,ug)Σ=ε(t((1+t)tug),ug)Σ=εtug2Σ,ω0,1, (2.37)
    γ(e,ug)Σ=γ((1+t)tug,ug)Σ=γ(ug,ug)Σ+γ(ug,(1+t)tug)Σ=γ2ug2Σ, (2.38)
    μ(e,ug)Σ=μ((1+t)tug,ug)Σ=μ2ug2Σ. (2.39)

    Collecting (2.36)–(2.39), we can obtain

    tug2Σ,ω0,1+εtug2Σ,ω0,1+γ2ug2Σ+μ2ug2Σ=(g,e)Σ=(g,(1+t)tug)ΣgΣtugΣ,ω0,22gΣtugΣ,ω0,1. (2.40)

    Then we get

    tugΣ,ω0,12gΣ,tugΣ,ω0,12εgΣ,ugΣ2γgΣ. (2.41)

    Next, based on the above estimates we deduce the estimate of ˜u. When g=˜u,e=˜u in (2.29) and utilizing integration by parts, we can see

    ˜u2Σ=(g,˜u)Σ=(t˜u,ug)Σ+ε(t˜u,ug)Σ+μ(˜u,ug)Σ+γ(˜u,ug)Σ=(t(IP1NΠM)u,ug)Σ+ε(t(IΠM)u,ug)Σ+γ((IP1N)u,ug)Σ=((IP1NΠM)(uu0),tug)Σε((IΠM)u,tug)Σ+γ((IP1N)u,ug)Σ(IP1NΠM)(uu0)Σ,ω0,1tugΣ,ω0,1+ε(IΠM)uΣ,ω0,1tugΣ,ω0,1+γ(IP1N)uΣugΣ. (2.42)

    Then we get

    ˜uΣC((IP1NΠM)(uu0)Σ,ω0,1+(IΠM)uΣ,ω0,1+(IP1N)uΣ)C((IP1N)(uu0)Σ,ω0,1+(IΠM)uΣ,ω0,1+(IP1N)(IΠM)uΣ,ω0,1+(IΠM)uΣ,ω0,1+(IP1N)uΣ). (2.43)

    Finally, according to the triangle inequality, we deduce

    uuLΣuUΣ+˜uΣuP1NΠMuΣ+˜uΣC((IP1N)uΣ+(IΠM)uΣ,ω0,1+(IP1N)(IΠM)uΣ,ω0,1+(IP1N)(uu0)Σ,ω0,1+(IΠM)uΣ,ω0,1). (2.44)

    Then, by Lemmas 2.1 and 2.2, we directly derive the final L2(Σ)-error estimates.

    In order to construct the multi-interval form of the Legendre space-time spectral fully discrete scheme, we take a1=1, aK+1=1, ak<ak+1 and denote Ik=(ak,ak+1], ck=ak+1ak, dk=ck/2, namely, I=Kk=1Ik, where K is a known positive integer.

    Denote

    v(t)2I,ωα,β=Ikvk(t)2Ik,˜ωα,β,v(t)L2ωα,β(I), (3.1)

    where vk(t)=v(t)|Ik,˜ωα,β=(1t)αdαk(21tdk)β(tIk) and the definition of vk(t)2Ik,˜ωα,β is presented in Section 2.

    Moreover, let Σk=Ω×Ik and denote

    v(x,y,t)2L2ωα,β(I;Hr(Ω))=Ikvk(x,y,t)2L2˜ωα,β(Ik;Hr(Ω)),v(x,y,t)L2ωα,β(I;Hr(Ω)), (3.2)

    where vk(t)=v(t)|Σk and the definition of vk(x,y,t)L2˜ωα,β(Ik;Hr(Ω)) is presented in Section 2.

    Let M=(M1,,MK) and L=(N,M). Define the space of trial and test functions in time

    XMK=YMKH1(I),YMK={ν:ν|IkPMk(Ik),1kK},˜XMK={ν:ν=(1t)q(t),q(t)YM1K}, (3.3)

    where PMk(Ik) is a space of polynomials of degree Mk on time span Ik and M1=(M11,,MK1).

    Then applying the Green's formula, we can write the multi-interval Legendre space-time spectral fully discrete scheme of (1.1) as: Find uKLV0NXMK satisfying

    {(tuKL,v)Σ+ε(tuKL,v)Σ+μ(uKL,v)Σ+γ(uKL,v)Σ=(f,v)Σ,vV0N˜XMK,uKL(x,y,1)=P1Nu0(x,y). (3.4)

    The stability analysis of (3.4) is similar to the single interval method, so we only provide the error analysis of the scheme. Firstly, we introduce the following definition and lemma of the multi-interval projection operator in time.

    Definition 3.1. [28] The orthogonal projection in time ΠM:H1(I)XMK is given by

    (t(ΠMuu),v)I=0,v˜XMK, (3.5)

    with ΠMu(1)=u(1).

    Lemma 3.1. [28] If uHσ(I) and σ1,ˉM=min1kKMk, we have

    lt(ΠMuu)I,ωl,l1CˉMl1Kk=1(d1kMk)1σσtukIk,l=0,1, (3.6)

    where uk=u|Ik.

    Denote UK=P1NΠMu. Now, we decompose the error into: uuKL=(uUK)+(UKuKL) and denote ˜uK=UKuKL. We note ˜uK(x,y,1)=0. Then according to the scheme (3.4), vV0N˜XMK, we have

    (t˜uK,v)Σ+ε(t˜uK,v)Σ+μ(˜uK,v)Σ+γ(˜uK,v)Σ=(t(UKu),v)Σ+ε(t(UKu),v)Σ+μ((UKu),v)Σ+γ(UKu,v)Σ. (3.7)

    By using the Definitions 2.1 and 3.1, we can see for some terms of the formula (3.7)

    (t(P1NΠMuu),v)Σ=(t(P1NΠMuP1Nu),v)Σ+(t(P1NI)u,v)Σ=(t(P1NI)u,v)Σ, (3.8)
    ε(t(P1NΠMuu),v)Σ=ε(t(ΠMP1NuP1Nu),v)Σ+ε((P1Ntutu,v)Σ=0, (3.9)
    μ((P1NΠMuu),v)Σ=μ((ΠMP1NuΠMu),v)Σ+μ((ΠMI)u,v)Σ=μ((ΠMI)u,v)Σ. (3.10)

    Then (3.7) can be simplified as follows:

    (t˜uK,v)Σ+ε(t˜uK,v)Σ+μ(˜uK,v)Σ+γ(˜uK,v)Σ=(t(P1NI)u,v)Σ+μ((ΠMI)u,v)Σ+γ((P1NΠMI)u,v)Σ,vV0N˜XMK. (3.11)

    Now, we give the L2(Σ)-error estimate of the multi-interval Legendre space-time spectral scheme.

    Theorem 3.1. Suppose u and uKL are the solutions of the problem (1.1) and scheme (3.4), respectively. If uHσ(I;Hr(Ω)H10(Ω)) for integers r,σ1, then

    uuKLΣC{Nr(utL2(I;Hr(Ω))+uL2(I;Hr(Ω)))+ˉM1Kk=1(d1kMk)1σ(σtukL2(Ik;L2(Ω))+σtukL2(Ik;L2(Ω))+NrσtukL2(Ik;Hr(Ω)))}, (3.12)

    where uk=u|Σk.

    If dk=d,Mk=M, we have

    uuKLΣC{Nr(utL2(I;Hr(Ω))+uL2(I;Hr(Ω)))+dMσ(σtuL2(I;L2(Ω))+σtuL2(I;L2(Ω))+NrσtuL2(I;Hr(Ω)))}. (3.13)

    Proof. Similar to the analysis of the L2(Σ)-error estimate of the single interval scheme, we also take the dual technique to give the proof. Considering the dual equation of (3.11), for a given gV0NXMK, we obtain ugV0N˜XMK such that

    (tw,ug)Σ+ε(tw,ug)Σ+μ(w,ug)Σ+γ(w,ug)Σ=(g,w)Σ,wV0N(YMKH(I)). (3.14)

    The existence and uniqueness of ug can be easily attained, so we focus on using the dual equation (3.14) to present the L2(Σ)-error estimate.

    In order to derive the estimate of ˜u, we take g=˜uK and w=˜uK in Eq (3.14),

    ˜uK2Σ=(g,˜uK)Σ=(t˜uK,ug)Σ+ε(t˜uK,ug)Σ+μ(˜uK,ug)Σ+γ(˜uK,ug)Σ=(t(P1NI)u,ug)Σ+μ((ΠMI)u,ug)Σ+γ((P1NΠMI)u,ug)Σ(P1NI)utΣ,ω1,0ugΣ,ω1,0+μ(ΠMI)uΣ,ω1,0ugΣ,ω1,0+γ(P1NΠMI)uΣ,ω1,0ugΣ,ω1,0. (3.15)

    Now, to deduce the estimates of of ug and ug, taking w=t111sugds1t[ω1,0ug] in Eq (3.14),

    ug2Σ,ω1,0+εug2Σ,ω1,0+μ21t[ω1,0ug]2Σ+γ21t[ω1,0ug]2Σ=(g,1t[ω1,0ug])ΣgΣ1t[ω1,0ug]Σ, (3.16)

    then we get

    1t[ω1,0ug]Σ2γgΣ,ugΣ,ω1,02γgΣ,ugΣ,ω1,02γεgΣ. (3.17)

    Taking estimates (3.17) into inequality (3.15) have

    ˜uK2ΣC((IP1N)utΣ,ω1,0gΣ+(ΠMI)uΣ,ω1,0gΣ+(ΠMP1NI)uΣ,ω1,0gΣ), (3.18)

    namely, error estimate of ˜uK is that

    ˜uKΣC((IP1N)utΣ,ω1,0+(ΠMI)uΣ,ω1,0+(ΠMP1NI)uΣ,ω1,0)C((IP1N)utΣ+(ΠMI)uΣ+(ΠMP1NI)uΣ). (3.19)

    Finally, according to the triangle inequality, we deduce

    uuKLΣuUKΣ+˜uKΣC((P1NI)utΣ+(ΠMI)uΣ,ω0,1+(ΠMP1NI)uΣ)C((P1NI)utΣ+(ΠMI)uΣ,ω0,1+(P1NI)uΣ+(ΠMI)uΣ,ω0,1+(P1NI)(ΠMI)uΣ,ω0,1). (3.20)

    In this section, we present the detailed implementation of the multi-interval case by taking Fourier-like basis functions in space and the appropriate basis functions in time. Regarding the single interval case, please see [26] for more information.

    Let

    XMkk={ν:ν=(1t)q(t),q(t)PMk1(Ik),tIk}u(k)(x,y,t):=uKL(x,y,t)|Σk,f(k)(x,y,t):=f(x,y,t)|Σk,1kK. (4.1)

    Then, according to the scheme (3.4), we can see for each k(1kK), we find u(k)V0NPMk(Ik) such that

    {(tu(k),v(k))Σk+ε(tu(k),v(k))Σk+μ(u(k),v(k))Σk+γ(u(k),v(k))Σk=(f(k),v(k))Σk,u(k)(x,y,ak)=u(k1)(x,y,ak),v(k)V0NXMkk, (4.2)

    where u(0)(x,y,a1)=P1Nu0(x,y).

    Furthermore, let

    u(k)(x,y,t)=w(k)(x,y,t)+u(k1)(x,y,ak),

    then the scheme (4.2) can be converted into: Find w(k)V0NPMk(Ik) such that

    {(tw(k),v(k))Σk+ε(tw(k),v(k))Σk+μ(w(k),v(k))Σk+γ(w(k),v(k))Σk=(f(k),v(k))Σkμ(u(k1)(ak),v(k))Σkγ(u(k1)(ak),v(k))Σk,v(k)V0NXMkk,w(k)(x,y,ak)=0. (4.3)

    In order to the operability of the scheme (4.3), we try to separate it into two parts: Find w(k)qV0NPMk(Ik)(q=1,2) such that

    {(tw(k)1,v(k))Σk+ε(tw(k)1,v(k))Σk+μ(w(k)1,v(k))Σk+γ(w(k)1,v(k))Σk=(f(k),v(k))Σkμ(P1Nu0,v(k))Σkγ(P1Nu0,v(k))Σk,v(k)V0NXMkk,w(k)1(x,y,ak)=0, (4.4)
    {(tw(k)2,v(k))Σk+ε(tw(k)2,v(k))Σk+μ(w(k)2,v(k))Σk+γ(w(k)2,v(k))Σk=μ(u(k1)(ak),v(k))Σk+γ(u(k1)(ak),v(k))Σkμ(P1Nu0,v(k))Σkγ(P1Nu0,v(k))Σk,w(k)2(x,y,ak)=0,v(k)V0NXMkk, (4.5)

    The solution of the scheme (4.3) is obtained by

    w(k)(x,y,t)=w(k)1(x,y,t)w(k)2(x,y,t). (4.6)

    The choice of basis functions ϕn(x) and ϕs(y)(0n,sN2) can refer [26]. Regarding the temporal local trail functions ψkm(t) and test functions ˜ψkm(t)(0mMk1), please see [28]. Let

    w(k)(x,y,t)=Mk1m=0N2n,s=0w(k)m,n,sϕn(x)ϕs(y)ψkm(t),W(k)=(w(k)m,n,s)Mk×(N1)2,w(k)q(x,y,t)=Mk1m=0N2n,s=0w(k)q,m,n,sϕn(x)ϕs(y)ψkm(t),W(k)=(w(k)q,m,n,s)Mk×(N1)2,q=1,2, (4.7)

    and v(k)(x,y,t)=ϕn(x)ϕs(y)˜ψ(k)m(t), where n,s=0,1,,N2 and m=0,1,,Mk1, we can see that W(k)=W(k)1W(k)2.

    Denote the sets of LGL points and weights in spatial directions by {x˜n,ϖ˜n}N+1˜n=0 and {y˜s,ϖ˜s}N+1˜s=0, and denote the set of LGL points in time span Ik by {t˜m,˙ϖ˜m}Mk+1˜m=0. Define

    ˜Ψ(k)=(˜ψ(k)m(t˜m))Mk×(Mk+2),F(k)=(f˜m,˜n,˜s)(Mk+2)×(N+2)2,f˜m,˜n,˜s=bk2(f(k)(x˜n,y˜s,t˜m)+μΔP1Nu0(x˜n,y˜s)γP1Nu0(x˜n,y˜s)). (4.8)

    Then, we can get the matrix form of the scheme (4.4) as follows:

    (C(k)+γD(k))W(k)1[Diag(λn)Diag(λs)]+(εC(k)+μD(k))W(k)1[IN1Diag(λn)+Diag(λs)IN1]=˜Ψ(k)Diag(˙ϖ˜m)F(k)[(ΦxDiag(ϖ˜n))T(ΦyDiag(ϖ˜s))T], (4.9)

    where matrices Diag(λn), Diag(λs), IN1, Φx and Φy in spatial directions are given in [26], and matrices C(k) and D(k)(1kK) in temporal direction are given in [28]. Then, by the properties of matrix multiplication in [32], Eq (4.9) can be formulated as

    A(k)vec(W(k)1)=vec(˜Ψ(k)Diag(˙ϖ˜m)F(k)[(ΦxDiag(ϖ˜n))T(ΦyDiag(ϖ˜s))T]), (4.10)

    where

    A(k)=Diag(λn)Diag(λs)(C(k)+γD(k))+[IN1Diag(λs)+Diag(λn)IN1](εC(k)+μD(k)). (4.11)

    Now, we try to get the matrix form of scheme (4.5). According to the processing means in [28], similarly, we have

    u(k1)(x,y,ak)=N2n,s=0ρ(k)n,sϕn(x)ϕs(y)+u(0)(x,y,a1),ρ(k)n,s=2k1l=1Ml1m=0w(l)m,n,s. (4.12)

    Then

    μ(u(k1)(ak),v(k))Σk+γ(u(k1)(ak),v(k))Σkμ(P1Nu0,v(k))Σkγ(P1Nu0,v(k))Σk=ρ(k)n,s(μ(λn+λs)+γλnλs)σ(k)m, (4.13)

    where {λn}N2n=0 are corresponding eigenvalues of mass matrices and {σ(k)m}Mk1m=0 can refer to (4.33) in [28]. Denote λn,s=μ(λn+λs)+γλnλs, then according to the values of {σ(k)m}Mk1m=0 we can get the matrix form of scheme (4.5) as follows:

    (C(k)+γD(k))W(k)2[Diag(λn)Diag(λs)]+(εC(k)+μD(k))W(k)2[IN1Diag(λs)+Diag(λn)IN1]=F(k), (4.14)

    where

    F(k)=(η(k)0,η(k)1,0,,0)T,η(k)i=(σ(k)iρ(k)n,sλn,s)1×(N1)2,i=0,1. (4.15)

    The Eq (4.14) above can also be converted to a form similar to Eq (4.10).

    In summary, the algorithm can be implemented as follows:

    (1) For each k1, compute W(k)1 by (4.9).

    (2) Obviously, W(1)=W(1)1. For each k2, assume that W(k1) have been obtained, then W(k)2 is obtained by (4.14) easily.

    (3) W(k)=W(k)1W(k)2 for each k2.

    We mainly devote this section to demonstrate the accuracy and efficiency of the multi-interval Legendre space-time spectral method by utilizing numerical examples for the 2D Sobolev equations. Regarding the numerical results of the single interval Legendre space-time spectral methods for the multi-dimensional Sobolev equations, one can refer [26].

    Example 5.1. We consider the 2D Sobolev equations (1.1) on the time interval I=(0,2] with the following exact solution:

    u(x,y,t)=e1t+0.2(sinπxsinπy+(1x2)(1y2)). (5.1)

    In this example, the two-interval Legendre space-time spectral method is applied to attain the numerical solution uL. We divide the time interval I=(0,2] into I1=(0,0.3] and I2=(0.3,2], namely, a1=0,a2=0.3 and a3=2. Under the premise of setting constants ε=μ=γ=1, we compare the images of numerical solutions uL and exact solutions u at different times in Figures 1 and 2. From these figures we can deduce that the image of the numerical solutions very well simulate the image of the exact solutions.

    Figure 1.  (Left) The exact solution u at time t=0.3. (Right) The numerical solution uL by two-interval Legendre spectral method at time t=0.3 for (M1,M2)=(40,40) and N=50. Take ε=μ=γ=1.
    Figure 2.  (Left) The exact solution u at time t=2. (Right) The numerical solution uL by two-interval Legendre spectral method at time t=2 for (M1,M2)=(60,60) and N=70. Take ε=μ=γ=1.

    To show the spectral accuracy of the proposed method, we plot the maximum point-wise errors and L2(Σ)-errors using semi-log coordinates in Figure 3. The numerical results indicate that the proposed method obtained the exponential convergence in both time and space.

    Figure 3.  Spectral accuracy. (Left) Temporal errors by taking N=15. (Right) Spatial errors by taking (M1,M2)=(35,35). Take ε=μ=γ=1.

    In Tables 1 and 2, we show L2(Σ)-errors in space and time, respectively, mainly to compare the numerical effects of the proposed method applying the Fourier-like basis functions and the traditional basis functions. We can then observe that multi-interval method taking Fourier-like basis functions attain better efficiency.

    Table 1.  Spatial errors. Take (M1,M2)=(40,40) and ε=μ=γ=1.
    N Fourier-like basis Standard basis Order
    uLu CPU(s) uLu CPU(s)
    12 1.4243E-05 0.3309 1.4243E-05 9.7683
    14 1.7056E-07 0.3463 1.7056E-07 26.4171 N28.71
    16 1.5700E-09 0.3552 1.5700E-09 55.0611 N35.11
    18 1.1462E-11 0.3734 1.1462E-11 122.2270 N41.77
    20 3.0801E-13 0.3892 3.0801E-13 237.8722 N34.32

     | Show Table
    DownLoad: CSV
    Table 2.  Temporal errors. Take N=20 and ε=μ=γ=1.
    M=(M1,M2) Fourier-like basis Standard basis Order
    uLu CPU(s) uLu CPU(s)
    (10, 10) 8.3000E-03 0.3431 8.3000E-03 4.6588
    (15, 15) 6.2018E-05 0.3495 6.2018E-05 13.9264 M12.08
    (20, 20) 7.0981E-07 0.3579 7.0981E-07 30.4591 M15.54
    (25, 25) 8.9560E-09 0.3621 8.9560E-09 61.2795 M19.59
    (30, 30) 1.0813E-10 0.3785 1.0813E-10 94.7840 M24.22
    (35, 35) 1.2687E-12 0.3862 1.2687E-12 150.8296 M28.84

     | Show Table
    DownLoad: CSV

    In Table 3, we compare the temporal L2(Σ)-errors obtained by using the single-interval Legendre spectral method and the two-interval Legendre spectral method for the same N and ε=μ=γ=1. One can find that the two-interval method show a great improvement in accuracy compared with the single-interval method.

    Table 3.  Temporal errors. Take ε=μ=γ=1.
    N Single interval method Two-interval method
    M uLu Order M=(M1,M2) uLu Order
    20 20 2.3710E-01 (10, 10) 8.3000E-03
    20 30 3.4000E-03 M10.47 (15, 15) 6.2018E-05 M12.08
    20 40 3.9093E-05 M15.52 (20, 20) 7.0981E-07 M15.54
    20 50 3.8171E-07 M20.74 (25, 25) 8.9560E-09 M19.59
    20 60 3.2869E-09 M26.08 (30, 30) 1.0813E-10 M24.23
    20 70 2.5627E-11 M31.49 (35, 35) 1.2687E-12 M28.84

     | Show Table
    DownLoad: CSV

    Example 5.2. In this example we consider the 2D Sobolev equations (1.1) on time interval I=(0,2] with the unknown exact solution. The source term is f=0 and the initial condition is taken as

    u0(x,y)={(1x2)sin(πy),(x,y)[1,0]×[1,1],(1y2)sin(πx),(x,y)(0,1]×[1,1]. (5.2)

    We also consider the two-interval Legendre space-time spectral method in time. We divide the time interval I=(0,2] into I1=(0,1.3] and I2=(1.3,2], namely, a1=0,a2=1.3 and a3=2. In Figure 4, we depict the numerical solutions uL(x,y,t) at t=1.3 and t=2 with (M1,M2)=(30,30),N=60 and ε=μ=γ=1.

    Figure 4.  The numerical solutions uL by two-interval Legendre space-time spectral method at (Left) t=1.3 and (Right) t=2 for (M1,M2)=(30,30) and N=60. Take ε=μ=γ=1.

    For the unknown of the exact solution, there is no uniform standard to compare the efficiency of the single interval Legendre space-time spectral method with the two-interval method. Thus in Figure 5 by taking the numerical solutions obtained under (M1,M2)=(30,30) and N=60 as a reference, we plot the temporal and spatial errors of the proposed method with ε=μ=γ=1. One can clearly observe that two-interval Legendre space-time spectral method possess the spectral accuracy both in time and space.

    Figure 5.  Spectral accuracy. (Left) Temporal errors by taking N=60. (Right) Spatial errors by taking (M1,M2)=(30,30). Take ε=μ=γ=1.

    Example 5.3. This example is devoted to exploring the 2D Sobolev equations (1.1) on time interval I=(0,1] with the exact solution, which is not regular enough and unknown in advance. The source term is f=0 and the initial condition is taken as, see Figure 6,

    u0(x,y)={max[0,0.30.5(|x+0.1|+|y+0.1|)]+0.5,(x0.5)2+(y0.5)2<0.2,max[0,0.30.5(|x+0.1|+|y+0.1|)],otherwise. (5.3)
    Figure 6.  Initial function u0(x,y).

    We divide the time interval I=(0,1] into I1=(0,0.7] and I2=(0.7,1] to consider the two-interval Legendre space-time spectral method. In Figure 7, we depict the numerical solutions uL(x,y,t) derived by the proposed method at t=0.7 and t=1 with (M1,M2)=(30,30),N=60 and ε=μ=γ=1.

    Figure 7.  The numerical solutions uL by two-interval Legendre space-time spectral method at (Left) t=0.7 and (Right) t=1 for (M1,M2)=(30,30) and N=60. Take ε=μ=γ=1.

    In Figure 8, by taking the numerical solutions obtained under (M1,M2)=(30,30) and N=60 as a reference, we present the temporal and spatial errors with ε=μ=γ=1. One can clearly observe that our method possess spectral accuracy both in time and space.

    Figure 8.  Spectral accuracy. (Left) Temporal errors by taking N=60. (Right) Spatial errors by taking (M1,M2)=(30,30). Take ε=μ=γ=1.

    As previously seen, the spectral method is commonly used to formulate the numerical scheme in space combined with the finite difference method in time, but in most cases, the infinite accuracy in space and finite accuracy in time leads to a unbalanced scheme. In order to avoid this problem, in this paper we use the Legendre-Galerkin method in space and the Legendre-tau-Galerkin method in time to study two-dimensional Sobolev equations. We have succeeded in obtaining spectral convergence in both time and space. In particular, the multi-interval form of the proposed method is also considered. In the theoretical analysis, we not only prove the stability of the single and multi-interval numerical scheme, but also give strict proof of the L2(Σ)-error estimates by using the dual technique, where a better error estimate is obtained for the single interval form and the optimal error estimate is obtained for multi-interval form. Compared with the single interval method, the multi-interval spectral method succeeds in reducing the scale of problems, adopting the parallel computers and improving the flexibility of algorithm. Another highlight of this paper is that the Fourier-like basis functions, different from the traditional basis functions, are adopted for the Legendre spectral method in space. Since the mass matrix obtained by Fourier-like basis functions is a diagonal matrix, the computing time and memory can be effectively reduced in the implementation of the algorithm. Numerical experiments show that our method can attain the spectral accuracy both in time and space, and the multi-interval method in time is more efficient than the single one.

    In a future study, we will extend our method to the numerical solutions of the nonlinear Sobolev equation by using appropriate technique to deal with the nonlinear terms effectively.

    This work was supported by the National Natural Science Foundation of China (12161063), Natural Science Foundation of Inner Mongolia Autonomous Regions (2021MS01018), Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region (NMGIRT2207).

    We declare no conflicts of interest in this paper.


    Acknowledgments



    No funding to report for the current study. RDD has received consulting fees from Jazz Pharmaceuticals. SF receives royalties from UptoDate/WoltersKluwer, and has been a paid speaker for Reckitt. RK has no conflicts of interest to report.

    Ethics approval of research



    Study procedures were determined as Exempt by the Boston Children's Hospital Institutional Review Board (IRB-P00036158 and IRB-P00036926).

    Conflict of interest



    The authors declare no conflicts of interest in this paper.

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