Solving the backward problem in Riesz–Feller fractional diffusion by a new nonlocal regularization method

Abstract

In this paper, we consider the backward problem introduced in [48] for Riesz–Feller fractional diffusion. To begin with, some basic properties of solution of the corresponding forward problem, such as the Lp estimates, symmetry property and asymptotic estimates, are established by Fourier analysis technique. And then, under various a priori bound assumptions, we give the L2 conditional stability estimates for the solution of backward problem and also its symmetry property. Moreover, in order to overcome the ill-posedness of the backward problem, we propose a new nonlocal regularization method (NLRM) to solve it. That is, the following nonlocal variational functional is introducedJ(φ)=12‖u(φ(x);x,T)−fδ(x)‖2+β2‖[Pα1⁎φ](x)‖2, where β∈(0,1) is a regularization parameter, “⁎” denotes the convolution operation and Pα1(x) is called a convolution kernel with parameter α1, which will be selected properly later. The minimizer of above variational problem is defined as the regularization solution, and the L2 estimates, symmetry property of regularization solution are given. These results actually show the well-posedness of nonlocal variational problem. Our idea is essentially that using this well-posed problem to approximate the backward (ill-posed) problem. Thus, under an a posteriori parameter choice rule, we deduce various convergence rate estimates under different a-priori bound assumptions for the exact solution. Finally, several numerical examples are given to show that the proposed numerical methods are effective and adaptive for different a-priori information.