Since the whole function U is also unknown, the first

inverse problem consists in determination a pair of functions (f, U) that satisfies (2.3), (2.4) and (2.5).

allows us to treat the problem as an ill-posed

inverse problem and solve it by regularization techniques.

This problem is one kind of

inverse problem, also called final value problem or time

inverse problem.

This task can be interpreted as an

inverse problem, which is often examined with respect to ill- or well-posedness.

In the present paper, we study the

inverse problem of determining the source term in a degenerate heat equation perturbed by a singular potential from the theoretical analysis and numerical computation angles.

The main aim of this paper is to solve the

inverse problem for the boundary value problem (1.1), (1.2) by Weyl function on a finite interval.

In this paper, we implement the Bayesian statistical inversion theory to obtain a solution for an

inverse problem of growth data using a fractional population growth model, defined in Section 2.

Mathematically, CT image reconstruction often can be formulated as a linear

inverse problem. For the detected measurements data b, the objective is to find the targeted image u from the following equation:

CS is a very appealing tool for

inverse problem in electromagnetism, as confirmed by the large number of papers published on relevant journals (see e.g., [21, 25-32]).

Knowing such data and employing an

inverse problem approach, the estimation of the suspension stiffness and damping coefficient could be feasible.

As a branch of the

inverse problem of heat transfer, inverse geometrical problem [1] of heat conduction has a broad application prospect in industrial equipment testing, nondestructive testing [2], geometry optimization [3], biological lesions [4], and other fields.