A random regularized approximate solution of the inverse problem for Burgers' equation

In this paper, we find a regularized approximate solution for an inverse problem for Burgers' equation. The solution of the inverse problem for Burgers' equation is ill-posed, i.e., the solution does not depend continuously on the data. The approximate solution is the solution of a regularized equation with randomly perturbed coefficients and randomly perturbed final value and source functions. To find the regularized solution, we use the modified quasi-reversibility method associated with the truncated expansion method with nonparametric regression. We also investigate the convergence rate.