2019
DOI: 10.1186/s13660-019-2262-9
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Determination of source term for the fractional Rayleigh–Stokes equation with random data

Abstract: In this article, we consider the problem of finding a source term of a Rayleigh-Stokes equation. Our problem is not well-posed in the sense of Hadamard. The sought solution does not depend continuously on the given data. Using the truncation method and some new techniques on trigonometric estimators, we give the regularized solution. Moreover, the mean square error and convergence rates are established.

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Cited by 8 publications
(12 citation statements)
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“…The formula is also contained in papers [10], equation (2.2), [14], equation (2.6) and [15], equation (8). Again, since these works are devoted to the study of other problems, the above issues were not considered.…”
Section: Forward Problemmentioning
confidence: 99%
See 1 more Smart Citation
“…The formula is also contained in papers [10], equation (2.2), [14], equation (2.6) and [15], equation (8). Again, since these works are devoted to the study of other problems, the above issues were not considered.…”
Section: Forward Problemmentioning
confidence: 99%
“…The inverse problems of determining the right-hand side (the heat source density) of the Rayleigh-Stokes equation have been considered by a number of authors (see, e.g., [8], [9], [10] and the bibliography therein). However, there is no general closed theory for the abstract case of the source function F (x, t).…”
mentioning
confidence: 99%
“…A review of some works in this direction is contained in the above-mentioned paper [1]. See also recent papers [13], [14] and references therein; 4) Many works are devoted to the study of the inverse problem of determining the right-hand side of the Rayleigh-Stokes equation (see, for example, [3], [15], [16], and the bibliography cited there). Since this inverse problem is Hadamard ill-posed, various regularization methods and numerical methods for finding the right-hand side of the equation are proposed in these works; 5) If the initial condition u(x, 0) = ϕ(x) in the problem (1.1) is replaced by u(x, T ) = ϕ(x), then the resulting problem is called backward problem.…”
Section: Introductionmentioning
confidence: 99%
“…Besides that, the study of problem (1.1) with random noise data also began to receive the attention of mathematicians. In [16], using the truncation method and some new techniques, the authors showed the regularized solution, and convergence rates were established. In [17], Triet et al investigated an inverse source problem (1.1) by a general filter method for random noise, the results for the study of problem (1.1) were rare.…”
Section: Introductionmentioning
confidence: 99%