
In this paper, we prove the existence of positive solutions with prescribed
−Δu−λu=(Iα∗F(u))f(u), x∈R3,
where
Citation: Shuai Yuan, Sitong Chen, Xianhua Tang. Normalized solutions for Choquard equations with general nonlinearities[J]. Electronic Research Archive, 2020, 28(1): 291-309. doi: 10.3934/era.2020017
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In this paper, we prove the existence of positive solutions with prescribed
−Δu−λu=(Iα∗F(u))f(u), x∈R3,
where
Let Ω be a bounded domain in Rd with sufficient smooth boundary ∂Ω. We consider the following initial boundary value problem (IBVP) for the anomalous diffusion model with multi-term time-fractional derivatives
{s∑j=1qj∂αj0+u(x,t)−(Lu)(x,t)=f(x)p(t),(x,t)∈Ω×I,u(x,0)=0,x∈Ω,u(x,t)=0,(x,t)∈∂Ω×I, | (1.1) |
where I=(0,T) with T>0 be fixed and ∂αj0+ is the Caputo fractional derivative defined by
∂αj0+u(x,t)=1Γ(1−αj)∫t0∂u(x,s)∂sds(t−s)αj,0<αj<1. |
For a fixed positive integer s, the orders α=(α1,...,αs) and the coefficients q=(q1,...,qs) are restricted in the admissible sets
B:={(α1,...,αs)∈Rs;¯α≥α1>α2>⋯>αs≥α_}, | (1.2) |
Q:={(q1,...,qs)∈Rs;q1=1,qj∈[q_,¯q],(j=2,...,s)}, | (1.3) |
with fixed 0<α_<¯α<1 and 0<q_<¯q.
−L is a symmetric uniformly elliptic operator defined on D(−L)=H2(Ω)∩H10(Ω) given by
Lu(x,t)=d∑i=1∂∂xi(d∑j=1aij(x)∂∂xju(x,t))+c(x)u(x,t),x∈Ω, |
in which the coefficients satisfy
aij=aji,1≤i,j≤d,aij∈C1(ˉΩ),μd∑i=1ξ2i≤d∑i,j=1aij(x)ξiξj,x∈ˉΩ,ξ=(ξ1,…,ξd)∈Rd,foraconstantμ>0,c(x)≤0,x∈ˉΩ,c(x)∈C(ˉΩ). |
If the number of fractional derivatives s, the orders α and its coefficients q, elliptic operator L and source functions p(t),f(x) are given appropriately, then IBVP Eq (1.1) is a direct problem. Here the spatial source term f(x) is unknown, so the inverse problem in the paper is to determine the spatial source term f(x) based on problem Eq (1.1) and an additional terminal data
u(x,T)=g(x),x∈Ω. | (1.4) |
Since the measurement is noise-contaminated inevitably, we denote the noisy measurement of g as gδ(x) satisfying
‖gδ(x)−g(x)‖≤δ. | (1.5) |
It is well known that the time-fractional diffusion equations (TFDEs) have a wide range of applications in physics, chemistry and other aspects [1,2,3,4,5]. The most representative is continuous time random walk problem in general non-Markovian processes. However, with the increasing demand on the accuracy of the problem, the single-time fractional diffusion equation gradually failed to meet the needs of the problem, so Schumer et al. [6] proposed using the multi-term time-fractional diffusion equation (MTFDE) to increase the accuracy of the model. MTFDE is not only a useful tool for describing the behavior of anomalous diffusion phenomena in highly heterogeneous aquifers and complex viscoelastic materials [7], but can also be applied indirectly in the numerical solution of distributed-order fractional differential equations [8].
There are many studies on the direct problem for IBVP Eq (1.1) in recent years such as some uniqueness and existence results [9], the maximum principle [10], and analytic solutions [11,12]. In terms of numerical work, there are also some papers on the numerical solutions by finite difference methods [13,14,15] and by finite element methods [16,17,18,19].
Due to the spectral method needs fewer grid points to obtain highly accurate solution. Therefore, the spectral method is more suitable for discrete MTFDE because of its huge computations. The study of the spectral method can be divided into two categories in MTFDE. On the one hand, the time fractional derivative is discreted by finite difference method and spectral method is applied for the space variable. Guo et al. [20] studied the numerical approximation of distributed-order time-space fractional reaction-diffusion equation. Jiang et al. [21] considered a Legendre spectral method on graded meshes for handling MTFDE with non-smooth solutions in two-dimensional case. Liu et al. [22] applied an alternating direction implicit Legendre spectral method to handle the multi-term time fractional Oldroyd-B fluid type diffusion equation in the two-dimensional case. On the other hand, the spectral method is applied both in space and in time. Zheng et al. [23] considered a high order numerical method for handling MTFDE. Zaky [24] applied a Legendre spectral tau method to handle MTFDE. However, we are interested not only in obtaining a numerical solution with high precision by using spectral method, but also in determining the spatial source term in an inverse problem of the MTFDE.
As far as we know, the theory as well as numerical methods of inverse source problems for single term (i.e, . s=1) case in Eq (1.1) are relatively abundant. Zhang et al. [25] testified a uniqueness result to inverse the spatial source term in one-dimensional case by using one-point Cauchy data and proposed an efficient numerical method. Wei et al. [26] studied to identify a spatial source term in a multi-dimensional time-fractional diffusion equation from boundary measured data. The uniqueness for the inverse source problem is proved by the Laplace transformation method. Yan et al. [27] studied to identify a spatial source term in a multi-dimensional time-fractional diffusion-wave equation from a part of noisy boundary data. The uniqueness of inverse spatial source term problem is proved by the Titchmarsh convolution theorem and the Duhamel principle. Sun et al. [28] devoted to recovering simultaneously the fractional order and the space-dependent source term from partial Cauchy's boundary data in a multi-dimensional time-fractional diffusion equation. Recently, Yeganeh et al. [29] came up with an interesting idea. They used a method based on a finite difference scheme in time and a local discontinuous Galerkin method in space to determine a spatial source term in a time-fractional diffusion equation. This has not been seen in previous studies.
For a multi-term case, however, research results on inverse source problem are relatively few at present. Jiang et al. [30] built a weak unique continuation property for time-fractional diffusion-advection equations, and they considered an inverse problem on determining identifying the spatial source term by interior measurements. Li et al. [31] considered an inverse problem of recovering time-dependent source term from the Cauchy data in a MTFDE, and they applied the conjugate gradient method to identify the approximate source term. Recently, Sun et al. [32] investigated an inverse the spatial source term in MTFDE with nohomogeneous boundary condition from partially disturbed boundary data. They proposed the Levenberg-Marquardt regularization method to compute an inverse source problem. In addition, simultaneous inversion of source term and other terms has been studied. For instance, Malik et al. [33] studied an inverse problem of identifying a time-dependent source term along with diffusion/temperature concentration from a non-local over-specified condition for a space-time fractional diffusion equation. Sun et al. [34] considered a nonlinear inverse problem for simultaneously recovering the potential function and the fractional orders in a MTFDE from the noisy boundary Cauchy data in the one-dimensional case. For other inverse source problems, we can refer to [35,36,37]. Nevertheless, to the best knowledge of the authors, no one has used the spectral method to determine a spatial source term in an inverse problem of MTFDE.
In this paper, we will focus on two aspect research on our proposed model Eq (1.1). One is the study on the direct problem. The theoretical analysis and the numerical scheme of the Galerkin spectral method are proposed to solve the IBVP Eq (1.1). On the other hand, we use the Galerkin spectral method to investigate an inverse spatial source term problem in a MTFDE from a noisy final data in a general bounded domain. In the propose method, multi-term Caputo fractional derivatives are discretized by the L1-formula and the Galerkin spectral method is applied for the space variable. At the same time, the comparison between the Galerkin spectral method and the finite difference method is added in this paper. Finally, we not only prove the validity of the Galerkin spectral method in the application of MTFDE, but also verify the superiority of the Galerkin spectral method in forward and inverse problems by comparing the numerical results with the finite difference method.
The remainder of this paper is organized as follows. Some preliminaries are presented in Section 2. The detailed convergence analysis of the presented method is shown in Section 3. Uniqueness and ill-posedness for the inverse problem are showed in Section 4. We present the Galerkin spectral method and the finite difference method algorithm in Section 5. Numerical results for four examples are investigated in Section 6. Finally, we give a conclusion in Section 7.
We firstly introduce some preliminaries as follows in this section.
Definition 2.1. The multinomial Mittag-Leffler function is defined by (see [9,38])
E(θ1,⋯,θs),θ0(z1,⋯,zs):=∞∑k=0∑k1+⋯+ks=k(k;k1,⋯,ks)∏sj=1zkjjΓ(θ0+∑sj=1θjkj), |
where θ0,θj∈R, and zj∈C (j=1,⋯,s), and (k;k1,⋯,ks) denotes the multinomial coefficient
(k;k1,⋯,ks):=k!k1!⋯ks!withk=s∑j=1kj, |
where kj(j=1,⋯,s) are non-negative integers.
For the convenience of the later, if the orders α=(α1,...,αs) and its coefficients q=(q1,...,qs) satisfy Eq (1.2) and Eq (1.3), then we adopt the abbreviation
E(n)α′,β(t):=E(α1,α1−α2,⋯,α1−αs),β(−λntα1,−q2tα1−α2,⋯,−qstα1−αs),t>0,n=1,2,…, |
where α′=(α1,α1−α2,⋯,α1−αs) and λn denotes the n-th eigenvalues of elliptic operator −L with the homogeneous Dirichlet boundary condition.
Lemma 2.2. ([9]) Let 0<αs<αs−1<⋯<α1<1. Then,
ddt{tα1E(n)α′,1+α1(t)}=tα1−1E(n)α′,α1(t),t>0. |
Lemma 2.3. ([9,39]) Let 0<αs<αs−1<⋯<α1<1. Then the function tα1−1E(n)α′,α1(t) is positive for t>0.
Lemma 2.4. ([40]) Let 0<αs<αs−1<⋯<α1<1. Then
|1−λntα1E(n)α′,α1+1(t)|≤s∑j=2M(1+qjtα1−αj)1+λntα1,t>0,n=1,2,…, |
where M ia a positive constant.
Proposition 2.5. Let 0<αs<αs−1<⋯<α1<1. Then we have E(n)α′,1+α1(t)>0.
Proof. By Lemma 2.2 and 2.3, we know that
ddt{tα1E(n)α′,1+α1(t)}=tα1−1E(n)α′,α1(t)>0. |
Hence it is obvious E(n)α′,1+α1(t)>0.
Proposition 2.6. For λn>0 and 0<αs<αs−1<⋯<α1<1, we have 0<1−λntα1E(n)α′,1+α1(t)<1 for t>0. Moreover, 1−λntα1E(n)α′,1+α1(t) is a strictly decreasing function on t>0.
Proof. By Lemma 2.2 and 2.3, we have
ddt{1−λntα1E(n)α′,1+α1(t)}=−λntα1−1E(n)α′,α1(t)<0. |
We notice that 1−λntα1E(n)α′,1+α1(t) is a continuous function on t. Hence, we have limt→0(1−λntα1E(n)α′,1+α1(t))=1. By Lemma 2.4 we know limt→∞(1−λntα1E(n)α′,1+α1(t))=0. The proof is completed.
Lemma 2.7. For λn>0 and 0<αs<αs−1<⋯<α1<1, such that
0≤E(n)α′,1+α1(T)≤1Tα1λn. |
Proof. By Proposition 2.6, we know 0<E(n)α′,1+α1(T)<1λnTα1.
Lemma 2.8. Let k be a positive integer, Δt>0 and limk→∞kΔt=T. We have the following properties from the sequence defined by
ωk=s∑j=1qjΔt−αjΓ(2−αj)(k1−αj−(k−1)1−αj), | (2.1) |
where αj and qj are defined in equation Eq (1.1). Here we denote ω−1k equivalent to 1ωk.
(1) ωk is a decreasing sequence with respect to k;
(2) ω1>⋯>ωk>0 for each k;
(3) ω−1k is an increasing sequence with respect to k;
(4) limk→∞k−α1ω−1k=Δtα1q1(1−α1)Γ(2−α1)+s∑j=2qjTα1−αj(1−αj)Γ(2−αj).
Proof. First, let Φ(x)=ωx be a continuous function with respect to x, According to dΦ(x)dx<0, we know Φ(x) is decreasing function, then we have ωk is decreasing the sequence. And we can direct to check that ω1>⋯>ωk>0 for each k. In addition, we can easy to derive ω−1k is increasing function. Further, we have
limk→∞k−α1ω−1k=limk→∞k−α1q1Δt−α1Γ(2−α1)(k1−α1−(k−1)1−α1)+s∑j=2qjΔt−αjΓ(2−αj)(k1−αj−(k−1)1−αj)=:limk→∞1Ψ1k+Ψ2k, |
where
Ψ1k=q1Δt−α1Γ(2−α1)(k1−α1−(k−1)1−α1)kα1,Ψ2k=s∑j=2qjΔt−αjΓ(2−αj)(k1−αj−(k−1)1−αj)kα1. |
First we have
limk→∞Ψ1k=limk→∞q1Δt−α1Γ(2−α1)(1−(1−1k)1−α1)k1−α1kα1=limk→∞q1Δt−α1Γ(2−α1)limk→∞(1−1+(1−α1)1k+o(1k))k=q1(1−α1)Γ(2−α1)Δt−α1, |
where we use the Taylor's formula of (1−1/x)1−α1 in the second equation. Similarly, we have
limk→∞Ψ2k=limk→∞s∑j=2qjΔt−αjΓ(2−αj)(k1−αj−(k−1)1−αj)kα1=limk→∞s∑j=2qjΔt−αjΓ(2−αj)(k1−αj−(k−1)1−αj)kαjk−αjΔt−α1Δtα1kα1=limk→∞s∑j=2qj1Γ(2−αj)(1−(1−1k)1−αj)k(kΔt)−αj(kΔt)α1Δt−α1=s∑j=2qjTα1−αj(1−αj)Γ(2−αj)Δt−α1. |
For all these reasons, we arrive at
limk→∞k−α1ω−1k=Δtα1q1(1−α1)Γ(2−α1)+s∑j=2qjTα1−αj(1−αj)Γ(2−αj). | (2.2) |
This proof is complete.
In this section, we discuss semi-discretize and full discretize schemes of the problem Eq (1.1) in the one-dimensional case. First, let tn:=nΔt,n=0,1,⋯,N, where Δt:=TN (at leat satisfying Δt<1) is the time step. We can denote un an approximation to u(x,tn), and in order to convenience we write p(tn),f(x),a(x),c(x) is equal to pn,f,a,c, where f(x),p(tn) are the source terms of Eq (1.1), and a(x),c(x) come from the definition of the symmetric uniformly elliptic operator L. Then we introduce the time-fractional derivative is approximated by the L1 formula [41].
s∑j=1qj∂αj0+u(x,tn)=n∑k=1ωk(u(x,tn−k+1)−u(x,tn−k))+rnΔt, |
where ωk=s∑j=1qjΔt−αjΓ(2−αj)(k1−αj−(k−1)1−αj) and rnΔt is the truncation error with the estimate
rnΔt≤cuΔt2−α1+⋯+cuΔt2−αs≤c1Δt2−α1. |
Here c1 is a constant depending on u,aj and T.
In this paper, we use H1-norm defined by
‖ν‖H1(Ω)=(‖ν‖2L2(Ω)+1ω1‖dνdx‖2L2(Ω))12. |
where ω1 is defined in Lemma 2.8.
In order to establish the complete semi-discrete problem, we define the error term rn by
rn:=1ω1[n∑j=1ωj(u(x,tn−j+1)−u(x,tn−j))−s∑j=1qj∂αj0+u(x,tn)]. | (3.1) |
Then we have
|rn|=1ω1|rnΔt|≤c2Δt2−α1, | (3.2) |
where c2 is a constant depending on u,aj and T.
Using the first term on right hand side of Eq (3.1) as an approximation of s∑j=1qj∂αj0+u(x,tn) leads to the following finite difference scheme to Eq (1.1)
n∑k=1ωk(un−k+1−un−k)−(a∂un∂x)x−cun=fpn. | (3.3) |
Then we have
un−1ω1(a∂un∂x)x−1ω1cun=n−1∑k=1ωk−ωk+1ω1un−k+ωnω1u0+1ω1fpn. | (3.4) |
Multiply both sides by ν∈H10(Ω) and integrate over Ω and by Green formula. We can find un∈H10(Ω), such that
(un,ν)+1ω1(a∂un∂x,∂ν∂x)−1ω1(cun,ν)=n−1∑k=1ωk−ωk+1ω1(un−k,ν)+ωnω1(u0,ν)+1ω1(fpn,ν). | (3.5) |
For convenience and without loss of generality, we consider the case aij(x)≡1,c(x)=0. Stability of the scheme Eq (3.5) is given in the following result.
Theorem 3.1. The semi-discretized problem Eq (3.5) is unconditionally stable in sense that for all Δt>0, holds
‖un‖H1(Ω)≤‖u0‖L2(Ω)+1ω1n∑k=1pk‖f‖L2(Ω). |
Proof. we prove this theorem using induction. When n = 1, scheme Eq (3.5) has
(u1,ν)+1ω1(∂u1∂x,∂ν∂x)=(u0,ν)+1ω1(1∑k=1p1f,ν). |
Taking ν=u1 and Schwarz inequality, we have
‖u1‖H1(Ω)≤‖u0‖L2(Ω)+1ω11∑k=1pk‖f‖L2(Ω). | (3.6) |
Now for the following inequality to hold
‖uj‖H1(Ω)≤‖u0‖L2(Ω)+1ω1j∑k=1pk‖f‖L2(Ω),j=1,2,⋯,n−1. | (3.7) |
We must prove that ‖un‖H1(Ω)≤‖u0‖L2(Ω)+1ω1∑nk=1pk‖f‖L2(Ω). Taking ν=un in Eq (3.5) and by using Eq (3.7), we have
‖un‖H1(Ω)≤n−1∑k=1ωk−ωk+1ω1‖un−k‖L2(Ω)+ωnω1‖u0‖L2(Ω)+1ω1‖f‖L2(Ω)pn≤[n−1∑k=1ωk−ωk+1ω1+ωnω1][‖u0‖L2(Ω)+1ω1n−1∑k=1pk‖f‖L2(Ω)]+1ω1pn‖f‖L2(Ω). | (3.8) |
Hence, we have ‖un‖H1(Ω)≤‖u0‖L2(Ω)+1ω1∑nk=1pk‖f‖L2(Ω).
Now, we give the convergence estimate between the exact solution and the solution of the semi-discretized problem.
Theorem 3.2. Let u(x,t) be the exact solution of Eq (1.1), {un}Nn=0 be the numerical solution of semi-discretized Eq (3.5), and u(x,t0)=u0. Then there holds the following error estimates
‖u(x,tn)−un‖H1(Ω)≤c2ω1Tα1Δt2−α1q1(1−α1)Γ(2−α1)+∑sj=2qjTα1−αj(1−αj)Γ(2−αj), |
where c2 is a constant depending on u,aj and T.
Proof. We will verify that the following estimate
‖u(x,tj)−uj‖H1(Ω)≤c2ω1ω−1jΔt2−α1,j=1,2,⋯,N. | (3.9) |
Here, we will prove it by the mathematical induction, let ˆen=u(x,tn)−un. For j=1, we have, by combining Eq (1.1), Eq (3.5), Eq (3.1), the error equation
(ˆe1,ν)+1ω1(∂ˆe1∂x,∂ν∂x)=(ˆe0,ν)+(r1,ν)ν∈H10(Ω). | (3.10) |
Taking ν=ˆe1, we have
‖u(x,t1)−u1‖H1(Ω)≤c2ω1ω−11Δt2−α1. |
Hence, Eq (3.9) is proven for the case j = 1. Now, Suppose the inequality Eq (3.9) holds for all j=1,2,⋯,n−1. Then we need to prove that it holds also for j=n.
Similar to j=1, we have ν∈H10(Ω)
(ˆen,ν)+1ω1(∂ˆen∂x,∂ν∂x)=n−1∑k=1ωk−ωk+1ω1(ˆen−k,ν)+ωnω1(ˆe0,ν)+(rn,ν). | (3.11) |
Taking ν=ˆen, and using Eq (3.9). We have
‖ˆen‖H1(Ω)≤n−1∑k=1ωk−ωk+1ω1‖ˆen−k‖L2(Ω)+ωnω1‖ˆe0‖L2(Ω)+‖rn‖L2(Ω)≤[n−1∑k=1ωk−ωk+1ω1+ωnω1]c2ω1ω−1nΔt2−α1. |
Then we obtain
‖ˆen‖H1(Ω)≤c2ω1ω−1nΔt2−α1. |
The estimate Eq (3.9) is proved. Notice that limk→∞kΔt=T is clearly true. Then, we have
‖u(x,tn)−un‖H1(Ω)≤c2ω1ω−1nΔt2−α1=c2ω1n−α1ω−1nΔt−α1Tα1Δt2−α1. | (3.12) |
Then by Eq (3.12) and Lemma 2.8, we deduce that for sufficently large n, the assertion of the theorem is valid.
First, we set
VM={ν∈H10(Ω)|ν∈PM(Ω)}, |
as the space of polynomials of degree ≤M with respect to x. Let P be the orthogonal projection operator from H10(Ω) into VM. Then for all ψ∈H10(Ω), we have Pψ∈VM. Further, we have ∀νM∈VM such that
(Pψ,νM)+1ω1(∂Pψ∂x,∂νM∂x)=(ψ,νM)+1ω1(∂ψ∂x,∂νM∂x). | (3.13) |
This the orthogonal projection operator satisfy the inequality [42]
‖ψ−Pψ‖H1(Ω)≤cM1−m‖ψ‖Hm(Ω), | (3.14) |
where ψ∈Hm(Ω)∩H10(Ω),m≥1.
Now, we consider the Galerkin scheme as follow: find unM∈VM, such that for all νM∈VM
(unM,νM)+1ω1(∂unM∂x,∂νM∂x)=n−1∑k=1ωk−ωk+1ω1(un−kM,νM)+ωnω1(u0M,νM)+pnω1(fM,νM). | (3.15) |
Theorem 3.3. Let u(x,t)∈H1((0,T),Hm(Ω)∩H10(Ω)),m>1 be the exact solution of Eq (1.1), {unM}Nn=0 be the numerical solution of full discretization Eq (3.15), and u0M=Pu0. Then there holds the following error estimates
‖u(x,tn)−unM‖H1(Ω)≤ω1Tα1(c2Δt2−α1+cM1−m‖u‖L∞(Hm))q1(1−α1)Γ(2−α1)+∑sj=2qjTα1−αj(1−αj)Γ(2−αj), |
where c2 is a constant depending on u,aj,T, and ‖u‖L∞(Hm):=supt∈(0,T)‖u(x,t)‖Hm(Ω).
Proof. From Eq (1.1), Eq (3.1) satisfy ∀ν∈H10(Ω),
(u(x,tn),ν)+1ω1(∂u(x,tn)∂x,∂ν∂x)=n−1∑k=1ωk−ωk+1ω1(u(x,tn−k),ν)+ωnω1(u(x,t0),ν)+(rn,ν)+pnω1(f,ν). | (3.16) |
By projecting u(x,tn) in Pu(x,tn)∈VM, applying Eq (3.13), we obtain for all νM∈VM
(Pu(x,tn),νM)+1ω1(∂Pu(x,tn)∂x,∂νM∂x)=n−1∑k=1ωk−ωk+1ω1(u(x,tn−k),νM)+ωnω1(u(x,t0),νM)+(rn,νM)+pnω1(f,νM). | (3.17) |
Let ˆϵnM=Pu(x,tn)−unM,ϵnM=u(x,tn)−unM by subtracting Eq (3.15) from Eq (3.17), we have
(ˆϵnM,νM)+1ω1(∂ˆϵnM∂x,∂νM∂x)=n−1∑k=1ωk−ωk+1ω1(ϵn−kM,νM)+ωnω1(ϵ0M,νM)+(rn,νM) | (3.18) |
Taking νM=ˆϵnM in above equation and using the triangular inequality ‖ϵnM‖H1(Ω)≤‖ˆϵnM‖H1(Ω)+‖u(x,tn)−Pu(x,tn)‖H1(Ω), we obtain
‖ϵnM‖H1(Ω)≤n−1∑k=1ωk−ωk+1ω1‖ϵn−kM‖H1(Ω)+‖rn‖H1(Ω)+‖u(x,tn)−Pu(x,tn)‖H1(Ω)≤n−1∑k=1ωk−ωk+1ω1‖ϵn−kM‖H1(Ω)+c2Δt2−α1+cM1−m‖u(x,tn)‖Hm(Ω). | (3.19) |
Here, we use the mathematical induction, by Eq (3.19), we can easy to verify when case of n = 1.
‖ϵ1M‖H1(Ω)≤ω1ω−11(c2Δt2−α1+cM1−m‖u(x,t1)‖Hm(Ω)) | (3.20) |
Suppose we have proven
‖ϵjM‖H1(Ω)≤ω1ω−1j(c2Δt2−α1+cM1−m‖u(x,tj)‖Hm(Ω))j=1,2,⋯,n−1. | (3.21) |
And then we need prove ‖ϵnM‖H1(Ω)≤ω1ω−1n(c2Δt2−α1+cM1−m‖u(x,tn)‖Hm(Ω)) By Eq (3.19), Eq (3.21) we have
‖ϵnM‖H1(Ω)≤[n−1∑k=1ωk−ωk+1ω1+ωnω1]ω1ω−1n[c2Δt2−α1+cM1−m‖u(x,tn)‖Hm(Ω)]. | (3.22) |
Finally, by limk→∞kΔt=T and the last equality have
‖u(x,tn)−unM‖H1(Ω)=ω1ω−1nn−α1Δt−α1Tα1(c2Δt2−α1+cM1−m‖u‖L∞(Hm)). | (3.23) |
Then by Eq (3.23) and Lemma 2.8, we deduce that for sufficently large n, the assertion of the theorem is valid.
Now we give error estimates for semi-discrete and full discrete problems in the following result.
Theorem 3.4. Let {un}Nn=0∈Hm(Ω)∩H10(Ω),m>1 is solution of semi-discrete problem Eq (3.5), {unM}Nn=0 is the solution of full discrete problem Eq (3.15), and u0M=Pu0. Then we have
‖un−unM‖H1(Ω)≤cω1Tα1M1−mmax1≤j≤n‖uj‖Hm(Ω)q1(1−α1)Γ(2−α1)+∑sj=2qjTα1−αj(1−αj)Γ(2−αj), |
Proof. According to the definition of P, for solution un of semi-discrete, we have
(Pun,νM)+1ω1(∂Pun∂x,∂νM∂x)=n−1∑k=1ωk−ωk+1ω1(un−k,νM)+ωnω1(u0,νM)+pnω1(f,νM). | (3.24) |
Let ˆenM=Pun−unM,enM=un−unM, by subtracting Eq (3.15) from Eq (3.24), we have
(ˆenM,νM)+1ω1(∂ˆenM∂x,∂νM∂x)=n−1∑k=1ωk−ωk+1ω1(en−kM,νM)+ωnω1(e0M,νM). | (3.25) |
Taking νM=ˆenM in above equation and using the triangular inequality ‖enM‖H1(Ω)≤‖ˆenM‖H1(Ω)+‖un−Pun‖H1(Ω), we obtain
‖enM‖H1(Ω)≤n−1∑k=1ωk−ωk+1ω1‖en−kM‖H1(Ω)+‖un−Pun‖H1(Ω)≤n−1∑k=1ωk−ωk+1ω1‖ϵn−kM‖H1(Ω)+cM1−m‖un‖Hm(Ω). | (3.26) |
Here, we use the mathematical induction similar to Theorem 3.3, and by limk→∞kΔt=T. We have follow result
‖un−unM‖H1(Ω)=ω1ω−1nn−α1Δt−α1Tα1cM1−mmax1≤j≤n‖uj‖Hm(Ω). | (3.27) |
Then by Eq (3.27) and Lemma 2.8, we deduce that for sufficently large n, the assertion of the theorem is valid.
In this section, we discuss the uniqueness of solution and the ill-posed analysis of the unknown source identification problem.
Denote the eigenvalues of the operator −L as λn and the corresponding eigenfunctions as φn∈H2(Ω)∩H10(Ω), then we have Lφn=−λnφn. Without loss of generality, suppose a family of eigenvalues 0<λ1≤λ2≤⋅⋅⋅≤λn≤⋅⋅⋅,limn→∞λn=+∞, then {φn}∞n=1 constituting an orthonormal basis in space L2(Ω).
Definition 4.1. ([43]) For any γ>0, define
D((−L)γ)={ψ∈L2(Ω);∞∑n=1λ2γn|(ψ,φn)|2<∞}, |
where (⋅,⋅) is the inner product in L2(Ω), and define its norm
‖ψ‖D((−L)γ)={∞∑n=1λ2γn|(ψ,φn)|2}12. |
Lemma 4.2. ([34]) For fixed α∈A,q∈Q, let f(x)∈Ld(0,T),p(t)∈D((−L)γ) with some d∈[1,∞] and γ∈[0,1]. Then Eq (1.1) exists a unique solution u(x,t) given by
u(x,t)=∞∑n=1fnQn(t)φn(x), | (4.1) |
where Qn(t)=∫t0p(s)(t−s)α1−1E(n)α′,α1(t−s)ds, and fn=(f,φn).
From Lemma 4.2, we know there exists a unique weak solution u∈L2((0,T);H2(Ω)∩H10(Ω)) for the direct problem Eq (1.1), if we know a source function f(x)p(t)∈L∞(0,T;L2(Ω)). And we know the formal solution for Eq (1.1) can be expressed by
u(x,t)=∞∑n=1fn∫t0p(s)(t−s)α1−1E(n)α′,α1(t−s)dsφn(x). | (4.2) |
Applying u(x,T)=g(x), we have
∞∑n=1gnφn(x)=g(x)=∞∑n=1fnQn(T)φn(x), | (4.3) |
where gn=(g,φn)andQn(T)=∫t0p(s)(T−s)α1−1E(n)α′,α1(T−s)ds.
Theorem 4.3. If p(t)∈C[0,T] satisfying p(t)≥p0>0 for all t∈[0,T], then the solution u(x,t),f(x) of problem Eq (1.1) is unique.
Proof. By Lemma 2.3, we have E(n)α′,α1(t)>0, for t>0. From Lemma 2.2 and Proposition 2.5, we know
Qn(T)≥p0∫T0(T−s)α1−1E(n)α′,α1(T−s)ds=p0Tα1E(n)α′,1+α1(T)>0. | (4.4) |
Hence, we know if g(x)=0, we have f(x)=0, and by Eq (4.2) we know u(x,t)=0. The proof is completed.
In the following, we show the inverse source problem Eq (1.1) is ill-posed. Without loss of generality, we take a final data gk(x)=φk(x)√λk in Eq (1.1) and then know ‖gk‖=1√λk→0,ask→∞. The corresponding source terms are fk(x)=φk(x)Qk(T)√λk, and we have ‖fk‖=1Qk(T)√λk. By Lemma 2.7, we have
Qk(T)≤‖p‖C[0,T]Tα1E(k)α′,1+α1(T)≤‖p‖C[0,T]λk. | (4.5) |
Hence ‖fk‖≥√λk‖q‖C[0,T]→∞, as k→∞. Then we know the inverse source problem Eq (1.1) is ill-posed.
For the sake of argument, we just talk about the one-dimensional case.
First, let LM(x) denotes the Legendre polynomial of degree M. ξj,j=1,2,⋯,M, are the Legendre-Gauss-Lobatto points, and these points satisfy (1−x2)L′M(x)=0. we consider the follow equation
{(unM,νM)+1ω1(a∂unM∂x,∂νM∂x)−1ω1(cunM,νM)=n−1∑k=1ωk−ωk+1ω1(un−kM,νM)+ωnω1(u0M,νM)+pnω1(fM,νM),(uNM,νM)=(gM,νM). | (5.1) |
We set
unM(x)=uM(x,nΔt)=M∑i=0δnihi(x), | (5.2) |
Here, hi is the Lagrangian polynomial defined in Ω i.e. hi∈VM,hi(ξj)=εij, with εij: the Krinecker-delta symbol. Since first equation in Eq (1.1) satisfies Dirichlet boundary condition, therefore, we have δn0=δnM=0. Take νM=hj(x),j=1,2,⋯,M−1, we can obtain
{(M−1∑i=1δnihi,hj)+1ω1(aM−1∑i=1δni∂hi∂x,∂hj∂x)−1ω1(cM−1∑i=1δnihi,hj)=n−1∑k=1ωk−ωk+1ω1(M−1∑i=1δn−kihi,hj)+ωnω1(M−1∑i=1δ0ihi,hj)+pnω1(fM,hj)(M−1∑i=1δNihi,hj)=(gM,hj). |
Denote
F=((fM,h1),⋯,(fM,hM−1))T,G=((gM,h1),⋯,(gM,hM−1))T, |
and set
δn=(δn1,⋯,δnM−1)T, |
By above equation, we have
{Kδn=1ω1pnF+n−1∑k=1ωk−ωk+1ω1ˆKδn−kˆKδN=G, | (5.3) |
where i,j=1,2,⋯,M−1, and
(K)ij=(hi,hj)+1ω1(a∂hi∂x,∂hj∂x)−1ω1(chi,hj),(ˆK)ij=(hi,hj). | (5.4) |
Here, we use the Legendre Gauss-type quadratures to approximate Eq (5.4), and by the first equation in Eq (5.3) and Eq (5.2), we can solve the forward problem.
For the inverse problem, by Eq (5.3), we have
{δ1=K−11ω1p1F=A1Fn=1δ2=K−1(1ω1p2F+(1−ω2ω1)ˆKδ1)=A2Fn=2⋮δN−1=AN−1Fn=N−1. | (5.5) |
For n=N, we have
{KδN=1ω1pNF+N−1∑k=1ωk−ωk+1ω1ˆKδN−kˆKδN=G, | (5.6) |
By Eq (5.5), Eq (5.6), we have
(5.7) |
And then by solving linear equation Eq (5.7), we can obtain . Further, we recover as an approximation of the source term by using a suitable integration rule.
In order to prove the ill-posedness of equation Eq (5.7), an upper bound of in Eq (5.7) is given below.
Theorem 5.1. Let the orthogonal basis function satisfy . Then the inverse matrix of the coefficient matrix A in Eq (5.7) have
(5.8) |
Proof. From the first equation of Eq (5.6), we can get
(5.9) |
Combining the second equation of Eq (5.6), we have
(5.10) |
Let , we have
(5.11) |
On the other hand, by Eq (5.5) we have
(5.12) |
By substituting Eq (5.12) into Eq (5.11), the equation is obtained, where
(5.13) |
Since is non-negative and decreasing with respect to , therefore we have
(5.14) |
The definition of tells us that the second part of is the higher order term of the first part. Therefore, we only need to estimate the matrix
(5.15) |
Due to , we only have to estimate the upper bound of . According to Eq (5.4), is a diagonal matrix, and
(5.16) |
Therefore, we have
(5.17) |
where is dependent on .
Remark 1. It can be seen from Theorem 5.1 that is completely determined by the projection dimension of the spectral method and the maximum and minimum norm of the basis function . It can be seen from Eq (5.8) that as the projection dimension increases, the numerator increases with respect to , while the denominator decreases with respect to . Therefore, the projection dimension can be used as regularization parameter. When the appropriate is selected, the minimum value of can be guaranteed, thus reducing the ill-posedness of the inverse problem.
Remark 2. Here we emphasize that the Galerkin spectral method is a projection method. In the projection method, as long as the appropriate dimension of the projection space is selected, the regularization effect can be effectively generated for the linear ill-posed problem, and it is no longer necessary to adopt other regularization techniques for the problem. This phenomenon is sometimes called self-regularization or regularization by projection. A series of descriptions and proofs of this phenomenon can be found in the literature [44].
In order to compare the finite difference method the and Galerkin spectral method in application of the multi-term time-fractional diffusion equation. We derive follow the scheme similar to reference [45].
In the finite difference algorithm, we can denote , where and
The space has the following discrete form:
for where with . In problem Eq (1.1), according to initial condition and boundary condition, we can get a numerical solution for forward problem Eq (1.1) from the finite difference scheme
(5.18) |
Denote then the scheme Eq (5.18) leads to the following iterative scheme
(5.19) |
where and is a tridiagonal matrix given by for and for and for
Inverse source problem based on finite difference. Similar to Section 5.1, we can derive a linear equation.
(5.20) |
where and is a matrix.
In this section, first, we verify the stability and validity of the proposed numerical methods. Without lose of generality, let the maximum time is . The noisy data are generated by adding random perturbations, i.e,
(6.1) |
where is relative noise level and generate random numbers uniformly distributed on . The corresponding noise level is calculated by . To show the accuracy of numerical solution, we compute the approximate -norm error denoted by
(6.2) |
and the approximate -norm relative error as
(6.3) |
where is term reconstructed and is the exact solution.
In addition, for the convenience of writing, we will abbreviate the Galerkin spectral method as GSM and the finite difference method as FDM.
Example 1. In this example, we firstly consider a three-term time fractional diffusion equation with a exact solution. Let and Take a source function and . Further more, we have exact analytical solution
(6.4) |
In the first three graphs of Figure 1, namely (a), (b) and (c), we show the errors in -norm and -norm for different time step in case of , and . Here fix the polynomial degree . In the same case, the graphs (d), (e) and (f) from Figure 1 show the the errors in -norm and -norm for different polynomial degree . Here we also take time step . From Figure 1, we can find that the theoretical convergence accuracy (see Theorem 3.3) is in good agreement with the numerical results.
In Table 1 and Figure 2, we make a comparison with FDM in terms of the forward problem by using the scheme Eq (5.1). Clearly, we observe that the scheme Eq (5.1) has higher accuracy than FDM on the forward problem. As a further step, we choose to obtain satisfactory results in time and accuracy.
approximation method | error | error | run time | |
GSM | 0.002121690 | 2.121690e-04 | 88.254s | |
FDM | 0.010039748 | 0.001419834 | 1.341s | |
GSM | 4.382322e-05 | 4.382322e-06 | 90.043s | |
FDM | 0.0085668512 | 0.001211535 | 10.076s | |
GSM | 8.535546e-07 | 8.535546e-08 | 90.622s | |
FDM | 0.008536392 | 0.001207228 | 96.931s | |
GSM | 1.913459e-08 | 1.913459e-09 | 198.829s | |
FDM | 0.008535798 | 0.001207144 | 1348.549s | |
GSM | 1.007804e-08 | 1.007804e-9 | 10834.238s | |
FDM | 0.008535787 | 0.001207142 | 33733.606s |
On the inverse source problem aspect. In order to avoid the impact of 'inverse crime'. We take to solve the direct problem by using the GSM Eq (5.1), and take to solve the inverse source problem by the scheme Eq (5.7). The related results are shown in (e), (f) of Figure 3 and Table 2. For the inverse source problem based on FDM. We take . Numerical result for with various noise levels are shown in (c), (d) of Figure 3.
0.082339 | ||
0.074294 | ||
0.059221 | ||
0.030306 | ||
0.034862 | ||
0.049156 | ||
0.014814 | ||
0.043858 | ||
0.043187 | ||
0.013495 | ||
0.010624 | ||
0.013953 |
Example 2. In this example, let . Take a exact source function and . We first solve the direct problem by using the GSM Eq (5.1) to obtain the additional data . Then we use the scheme Eq (5.7) to solve inverse source problem. Numerical result for , and with various noise levels are presented in (e), (f) of Figure 4 and Table 3. For the inverse source problem based on FDM. We take . Numerical result for with various noise levels are shown in (c), (d) of Figure 4.
0.085368 | ||
0.092676 | ||
0.088179 | ||
0.076024 | ||
0.082049 | ||
0.079423 | ||
0.073424 | ||
0.0800324 | ||
0.077136 | ||
0.0652520 | ||
0.066370 | ||
0.066352 |
Example 3. In this example, let . We consider a continuous piecewise smooth function.
(6.5) |
We first solve the direct problem by using the GSM Eq (5.1) to obtain the additional data . Then we use the scheme Eq (5.7) to solve inverse source problem. Numerical result for , and with various noise levels are presented in (e), (f) of Figure 5 and Table 4. For the inverse source problem based on FDM. We take . Numerical result for with various noise levels are shown in (c), (d) of Figure 5.
0.045909 | ||
0.058139 | ||
0.058454 | ||
0.042321 | ||
0.048615 | ||
0.047175 | ||
0.040261 | ||
0.048302 | ||
0.046372 | ||
0.012787 | ||
0.011516 | ||
0.011305 |
Example 4. Let . Take a exact source function and . We first solve the direct problem by using the GSM Eq (5.1) to obtain the additional data . Then we use the scheme Eq (5.7) to solve inverse source problem. Numerical result for , and with various noise levels are presented in (e), (f) of Figure 6 and Table 5. For the inverse source problem based on FDM. We take . Numerical result for with various noise levels are shown in (c), (d) of Figure. 6.
0.100485 | ||
0.111043 | ||
0.107902 | ||
0.093797 | ||
0.068835 | ||
0.064510 | ||
0.072740 | ||
0.073513 | ||
0.072786 | ||
0.034704 | ||
0.037078 | ||
0.035328 |
By observing Figures 3–6, we can see that denotes the graph of the numerical solution of the forward problem, namely the input data. is the numerical approximation of the inverse source problem without noisy data. and are results of the inverse source problem based on the FDM with and without regularization method, and and are results of the inverse source problem based on the GSM under the same case.
From Tables 6–9, we show the error levels of two numerical reconstruction method with and without regularization method. We can find that the numerical results of the inverse problem based on FDM with regularization method are obviously better than those without regularization method. However, the numerical results of the inverse problem based on the GSM with and without regularization method do not show a big difference compared with the numerical results of the FDM. There may even be cases where regularization method is not required to get a better result. Hence, we can conclude that scheme Eq (5.7) itself has regularization function compared to the FDM in the inverse source problem. Next, we explain why scheme Eq (5.7) is not sensitive to noise level.
method | ||||
GSM (no regularization) | 0.2501 | 0.0832 | 0.0290 | 0.0079 |
GSM (regularization) | 0.0823 | 0.0303 | 0.0134 | 0.0148 |
FDM (no regularization) | 4.3792 | 0.7871 | 0.4036 | 0.0965 |
FDM (regularization) | 0.0301 | 0.0052 | 0.0025 | 6.273e-4 |
method | ||||
GSM (no regularization) | 0.0944 | 0.0635 | 0.0119 | 0.0010 |
GSM (regularization) | 0.0853 | 0.0760 | 0.0734 | 0.0652 |
FDM (no regularization) | 2.6407 | 1.220 | 0.2478 | 0.027 |
FDM (regularization) | 0.2043 | 0.0520 | 0.0208 | 0.0164 |
method | ||||
GSM (no regularization) | 0.4327 | 0.2868 | 0.0365 | 0.0126 |
GSM (regularization) | 0.0459 | 0.0423 | 0.0402 | 0.0127 |
FDM (no regularization) | 11.808 | 5.0791 | 1.4387 | 0.1025 |
FDM (regularization) | 0.0353 | 0.0239 | 0.0116 | 0.0102 |
method | ||||
GSM (no regularization) | 0.2924 | 0.0878 | 0.0425 | 0.0233 |
GSM (regularization) | 0.1004 | 0.0937 | 0.0727 | 0.0347 |
FDM (no regularization) | 2.79904 | 1.1476 | 0.2951 | 0.1435 |
FDM (regularization) | 0.1435 | 0.0846 | 0.0181 | 0.0014 |
In Table 10, we show of matrices , and for Example 1. Obviously, is small and has a reasonable size. Therefore numerical solutions are not sensitive with respect to the perturbation in the initial data. But, In Table 11, The numerical format of FDM is sensitive to the perturbation in the initial datal. Therefore, GSM has better anti-interference than FDM. On the other hand, in Tables 12 and 13, We can find that although GSM has better anti-interference than FMD, it can not completely get rid of the regularization method when the unknown source has less regularity and the meased data has a high noise level.
0.05 | 0.356708 | 86.11457 | 0.369712 |
0.01 | 0.360401 | 86.11457 | 0.360933 |
0.005 | 0.360111 | 86.11457 | 0.360085 |
0.001 | 0.360341 | 86.11457 | 0.360345 |
0.0001 | 0.360348 | 86.11457 | 0.360348 |
0.05 | 7.0881 | 2.322e+02 | 27.8227 |
0.01 | 7.0743 | 2.322e+02 | 8.8667 |
0.005 | 7.0809 | 2.322e+02 | 7.5734 |
0.001 | 7.0804 | 2.322e+02 | 7.0929 |
0.0001 | 7.0796 | 2.322e+02 | 7.0712 |
0.01 | 0.013187 | 1.5104e+04 | 0.161897 |
0.005 | 0.013226 | 1.5104e+04 | 0.164711 |
0.001 | 0.013227 | 1.5104e+04 | 0.159407 |
0.0001 | 0.013228 | 1.5104e+04 | 0.159405 |
0.01 | 0.459280 | 3.9992e+04 | 76.739424 |
0.005 | 0.459857 | 3.9992e+04 | 27.761398 |
0.001 | 0.459380 | 3.9992e+04 | 7.957420 |
0.0001 | 0.459332 | 3.9992e+04 | 5.798087 |
In this paper, we first obtain a high accuracy numerical solution by using the GSM, and give the error estimates between exact solution and semi-discrete solution as well as full-discrete one, and compare with the FDM. It is indicate that our method has a better accuracy. Secondly, the GSM is extended to solve the inverse source problem. Moreover, we find that this method can effectively reduce the ill-posednes of inverse source problem compared with the traditional FDM. Thus the spectral method itself can play a regularization role. It should be mentioned that the estimates given in the paper are also valid in two and three dimensional cases. In the following work, we will continue to try to optimize the GSM numerical scheme so that it can better play the role of regularization method. For example, replacing the original basis functions with smooth periodic functions or equidistant trigonometric functions to avoid Runge phenomenon.
This work is supported by the NSF of China (grant no. 12201502), the Youth Science and Technology Fund of Gansu Province (grant no. 20JR10RA099), the Innovation Capacity Improvement Project for Colleges and Universities of Gansu Province (grant no. 2020B-088) and the Innovation star of Gansu Province (grant no. 2022CXZX-324).
The authors declare there is no conflict of interest.
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1. | Gabriel García-Laiton, Fernando Arcenio Zubieta López, Ehsan Shakerzadeh, Ernesto Chigo-Anota, Role of homonuclear B–B/N–N bonds in DNA nucleobases adsorption on boron nitride fullerenes: Biosensor and drug transport implications, 2025, 2210271X, 115188, 10.1016/j.comptc.2025.115188 |
approximation method | error | error | run time | |
GSM | 0.002121690 | 2.121690e-04 | 88.254s | |
FDM | 0.010039748 | 0.001419834 | 1.341s | |
GSM | 4.382322e-05 | 4.382322e-06 | 90.043s | |
FDM | 0.0085668512 | 0.001211535 | 10.076s | |
GSM | 8.535546e-07 | 8.535546e-08 | 90.622s | |
FDM | 0.008536392 | 0.001207228 | 96.931s | |
GSM | 1.913459e-08 | 1.913459e-09 | 198.829s | |
FDM | 0.008535798 | 0.001207144 | 1348.549s | |
GSM | 1.007804e-08 | 1.007804e-9 | 10834.238s | |
FDM | 0.008535787 | 0.001207142 | 33733.606s |
0.082339 | ||
0.074294 | ||
0.059221 | ||
0.030306 | ||
0.034862 | ||
0.049156 | ||
0.014814 | ||
0.043858 | ||
0.043187 | ||
0.013495 | ||
0.010624 | ||
0.013953 |
0.085368 | ||
0.092676 | ||
0.088179 | ||
0.076024 | ||
0.082049 | ||
0.079423 | ||
0.073424 | ||
0.0800324 | ||
0.077136 | ||
0.0652520 | ||
0.066370 | ||
0.066352 |
0.045909 | ||
0.058139 | ||
0.058454 | ||
0.042321 | ||
0.048615 | ||
0.047175 | ||
0.040261 | ||
0.048302 | ||
0.046372 | ||
0.012787 | ||
0.011516 | ||
0.011305 |
0.100485 | ||
0.111043 | ||
0.107902 | ||
0.093797 | ||
0.068835 | ||
0.064510 | ||
0.072740 | ||
0.073513 | ||
0.072786 | ||
0.034704 | ||
0.037078 | ||
0.035328 |
method | ||||
GSM (no regularization) | 0.2501 | 0.0832 | 0.0290 | 0.0079 |
GSM (regularization) | 0.0823 | 0.0303 | 0.0134 | 0.0148 |
FDM (no regularization) | 4.3792 | 0.7871 | 0.4036 | 0.0965 |
FDM (regularization) | 0.0301 | 0.0052 | 0.0025 | 6.273e-4 |
method | ||||
GSM (no regularization) | 0.0944 | 0.0635 | 0.0119 | 0.0010 |
GSM (regularization) | 0.0853 | 0.0760 | 0.0734 | 0.0652 |
FDM (no regularization) | 2.6407 | 1.220 | 0.2478 | 0.027 |
FDM (regularization) | 0.2043 | 0.0520 | 0.0208 | 0.0164 |
method | ||||
GSM (no regularization) | 0.4327 | 0.2868 | 0.0365 | 0.0126 |
GSM (regularization) | 0.0459 | 0.0423 | 0.0402 | 0.0127 |
FDM (no regularization) | 11.808 | 5.0791 | 1.4387 | 0.1025 |
FDM (regularization) | 0.0353 | 0.0239 | 0.0116 | 0.0102 |
method | ||||
GSM (no regularization) | 0.2924 | 0.0878 | 0.0425 | 0.0233 |
GSM (regularization) | 0.1004 | 0.0937 | 0.0727 | 0.0347 |
FDM (no regularization) | 2.79904 | 1.1476 | 0.2951 | 0.1435 |
FDM (regularization) | 0.1435 | 0.0846 | 0.0181 | 0.0014 |
0.05 | 0.356708 | 86.11457 | 0.369712 |
0.01 | 0.360401 | 86.11457 | 0.360933 |
0.005 | 0.360111 | 86.11457 | 0.360085 |
0.001 | 0.360341 | 86.11457 | 0.360345 |
0.0001 | 0.360348 | 86.11457 | 0.360348 |
0.05 | 7.0881 | 2.322e+02 | 27.8227 |
0.01 | 7.0743 | 2.322e+02 | 8.8667 |
0.005 | 7.0809 | 2.322e+02 | 7.5734 |
0.001 | 7.0804 | 2.322e+02 | 7.0929 |
0.0001 | 7.0796 | 2.322e+02 | 7.0712 |
0.01 | 0.013187 | 1.5104e+04 | 0.161897 |
0.005 | 0.013226 | 1.5104e+04 | 0.164711 |
0.001 | 0.013227 | 1.5104e+04 | 0.159407 |
0.0001 | 0.013228 | 1.5104e+04 | 0.159405 |
0.01 | 0.459280 | 3.9992e+04 | 76.739424 |
0.005 | 0.459857 | 3.9992e+04 | 27.761398 |
0.001 | 0.459380 | 3.9992e+04 | 7.957420 |
0.0001 | 0.459332 | 3.9992e+04 | 5.798087 |
approximation method | error | error | run time | |
GSM | 0.002121690 | 2.121690e-04 | 88.254s | |
FDM | 0.010039748 | 0.001419834 | 1.341s | |
GSM | 4.382322e-05 | 4.382322e-06 | 90.043s | |
FDM | 0.0085668512 | 0.001211535 | 10.076s | |
GSM | 8.535546e-07 | 8.535546e-08 | 90.622s | |
FDM | 0.008536392 | 0.001207228 | 96.931s | |
GSM | 1.913459e-08 | 1.913459e-09 | 198.829s | |
FDM | 0.008535798 | 0.001207144 | 1348.549s | |
GSM | 1.007804e-08 | 1.007804e-9 | 10834.238s | |
FDM | 0.008535787 | 0.001207142 | 33733.606s |
0.082339 | ||
0.074294 | ||
0.059221 | ||
0.030306 | ||
0.034862 | ||
0.049156 | ||
0.014814 | ||
0.043858 | ||
0.043187 | ||
0.013495 | ||
0.010624 | ||
0.013953 |
0.085368 | ||
0.092676 | ||
0.088179 | ||
0.076024 | ||
0.082049 | ||
0.079423 | ||
0.073424 | ||
0.0800324 | ||
0.077136 | ||
0.0652520 | ||
0.066370 | ||
0.066352 |
0.045909 | ||
0.058139 | ||
0.058454 | ||
0.042321 | ||
0.048615 | ||
0.047175 | ||
0.040261 | ||
0.048302 | ||
0.046372 | ||
0.012787 | ||
0.011516 | ||
0.011305 |
0.100485 | ||
0.111043 | ||
0.107902 | ||
0.093797 | ||
0.068835 | ||
0.064510 | ||
0.072740 | ||
0.073513 | ||
0.072786 | ||
0.034704 | ||
0.037078 | ||
0.035328 |
method | ||||
GSM (no regularization) | 0.2501 | 0.0832 | 0.0290 | 0.0079 |
GSM (regularization) | 0.0823 | 0.0303 | 0.0134 | 0.0148 |
FDM (no regularization) | 4.3792 | 0.7871 | 0.4036 | 0.0965 |
FDM (regularization) | 0.0301 | 0.0052 | 0.0025 | 6.273e-4 |
method | ||||
GSM (no regularization) | 0.0944 | 0.0635 | 0.0119 | 0.0010 |
GSM (regularization) | 0.0853 | 0.0760 | 0.0734 | 0.0652 |
FDM (no regularization) | 2.6407 | 1.220 | 0.2478 | 0.027 |
FDM (regularization) | 0.2043 | 0.0520 | 0.0208 | 0.0164 |
method | ||||
GSM (no regularization) | 0.4327 | 0.2868 | 0.0365 | 0.0126 |
GSM (regularization) | 0.0459 | 0.0423 | 0.0402 | 0.0127 |
FDM (no regularization) | 11.808 | 5.0791 | 1.4387 | 0.1025 |
FDM (regularization) | 0.0353 | 0.0239 | 0.0116 | 0.0102 |
method | ||||
GSM (no regularization) | 0.2924 | 0.0878 | 0.0425 | 0.0233 |
GSM (regularization) | 0.1004 | 0.0937 | 0.0727 | 0.0347 |
FDM (no regularization) | 2.79904 | 1.1476 | 0.2951 | 0.1435 |
FDM (regularization) | 0.1435 | 0.0846 | 0.0181 | 0.0014 |
0.05 | 0.356708 | 86.11457 | 0.369712 |
0.01 | 0.360401 | 86.11457 | 0.360933 |
0.005 | 0.360111 | 86.11457 | 0.360085 |
0.001 | 0.360341 | 86.11457 | 0.360345 |
0.0001 | 0.360348 | 86.11457 | 0.360348 |
0.05 | 7.0881 | 2.322e+02 | 27.8227 |
0.01 | 7.0743 | 2.322e+02 | 8.8667 |
0.005 | 7.0809 | 2.322e+02 | 7.5734 |
0.001 | 7.0804 | 2.322e+02 | 7.0929 |
0.0001 | 7.0796 | 2.322e+02 | 7.0712 |
0.01 | 0.013187 | 1.5104e+04 | 0.161897 |
0.005 | 0.013226 | 1.5104e+04 | 0.164711 |
0.001 | 0.013227 | 1.5104e+04 | 0.159407 |
0.0001 | 0.013228 | 1.5104e+04 | 0.159405 |
0.01 | 0.459280 | 3.9992e+04 | 76.739424 |
0.005 | 0.459857 | 3.9992e+04 | 27.761398 |
0.001 | 0.459380 | 3.9992e+04 | 7.957420 |
0.0001 | 0.459332 | 3.9992e+04 | 5.798087 |