2018
DOI: 10.1186/s13661-018-1030-y
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The quasi-boundary value regularization method for identifying the initial value with discrete random noise

Abstract: In this paper, we study an inverse initial value problem for the fractional diffusion equation with discrete noise. This problem is ill-posed in the sense of Hadamard. We apply the trigonometric method in a nonparametric regression associated with the quasi-boundary value regularization method to deal with this ill-posed problem. The corresponding convergence estimate for this method is obtained. The numerical results show that this regularization method is flexible and stable. MSC: 35R25; 47A52; 35R30

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Cited by 16 publications
(8 citation statements)
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“…The truncation method in this paper is similar to the method in [27,18]. The quasi-boundary value method in this section is more effective and useful than the one in [28]. The advantage of this method is that it allows us to estimate the norm on the Hilbert scales H σ (Ω).…”
Section: Convergence Resultsmentioning
confidence: 98%
See 1 more Smart Citation
“…The truncation method in this paper is similar to the method in [27,18]. The quasi-boundary value method in this section is more effective and useful than the one in [28]. The advantage of this method is that it allows us to estimate the norm on the Hilbert scales H σ (Ω).…”
Section: Convergence Resultsmentioning
confidence: 98%
“…where W k = W k1,k2,...,k d are mutually independent random variables, W k ∼ N (0, 1) and ε k = ε k1,k2,...,k d are positive constants bounded by a positive constant ε max . Some inverse problems when d = 1 were studied in [6,21,28]. Our main contributions in this paper is as follows:…”
Section: Introductionmentioning
confidence: 99%
“…The quasi-boundary value method (QBVM), also referred to as the non-local boundary value method, has been widely used in solving ill-posed inverse problems for parabolic equations, see e.g., [2,13,11,19] and the references therein. Recently, it has been successfully generalized for solving the fractional diffusion inverse problems [42,48,49,50]. Instead of working on (1) and (2), the method deals with a well-posed problem D α t v(x, t) = (Lv)(x, t) + f µ (x)q(t), x ∈ Ω, t ∈ (0, T ), v(x, t) = 0,…”
Section: Regularization For the Inverse Problemsmentioning
confidence: 99%
“…For applications of partial differential equations and other areas with conformable derivatives, we refer the reader to previous works 1–5,8–28 and the references therein. However, we note 29 (fix y>0 and αfalse(0,1false]) a function f:false[0,false)double-struckR has a conformable derivative scriptTαffalse(yfalse) if and only if f is differentiable at y and scriptTαffalse(yfalse)=y1α0.3emffalse(yfalse).…”
Section: Introductionmentioning
confidence: 99%