In this paper, the problem of finding the source function for the Rayleigh–Stokes equation is considered. According to Hadamard’s definition, the sought solution of this problem is both unstable and independent of continuous data. By using the fractional Tikhonov method, we give the regularized solutions and then deal with a priori error estimate between the exact solution and its regularized solutions. Finally, the proposed regularized methods have been verified by simple numerical experiments to check error estimate between the sought solution and the regularized solution.
<abstract><p>The main goal of this work is to study a regularization method to reconstruct the solution of the backward non-linear hyperbolic equation $ u_{tt} + \alpha\Delta^2u_t +\beta \Delta ^2u = \mathcal{F}(x, t, u) $ come with the input data are blurred by random Gaussian white noise. We first prove that the considered problem is ill-posed (in the sense of Hadamard), i.e., the solution does not depend continuously on the data. Then we propose the Fourier truncation method for stabilizing the ill-posed problem. Base on some priori assumptions for the true solution we derive the error and a convergence rate between a mild solution and its regularized solutions. Also, a numerical example is provided to confirm the efficiency of theoretical results.</p></abstract>
<abstract><p>In this paper, we are interested in diffusion equations with conformable derivatives with variable order. We will study two different types of models: the initial value model and the nonlocal in time model. With different values of input values, we investigate the well-posedness of the mild solution in suitable spaces. We also prove the convergence of mild solution of the nonlocal problem to solutions of the initial problem. The main technique of our paper is to use the theory of Fourier series in combination with evaluation techniques for some generalized integrals. Our results are one of the first directions on the diffusion equation with conformable variable derivative in Hilbert scales.</p></abstract>
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