# A regularization approach for an inverse source problem in elliptic systems from single Cauchy data

## TL;DR

This paper develops a Tikhonov-type regularization method for identifying sources in elliptic systems from single boundary measurements, providing convergence analysis, error bounds, and a numerical solution approach.

## Contribution

It introduces a novel regularization approach for inverse elliptic problems with theoretical convergence and error analysis, supported by numerical experiments.

## Key findings

- Convergence of finite element approximations to the inverse problem solutions.
- Derivation of error bounds and convergence rates under source conditions.
- Numerical validation using a conjugate gradient method.

## Abstract

In this paper we investigate the problem of identifying the source term in an elliptic system from a single noisy measurement couple of the Neumann and Dirichlet data. A variational method of Tikhonov-type regularization with specific misfit term of Kohn-Vogelius-type and quadratic stabilizing penalty term is suggested to tackle this linear inverse problem. The method also appears as a variant of the Lavrentiev regularization. For the occurring linear inverse problem in infinite dimensional Hilbert spaces, convergence and rate results can be found from the general theory of classical Tikhonov and Lavrentiev regularization. Using the variational discretization concept, where the PDE is discretized with piecewise linear and continuous finite elements, we show the convergence of finite element approximations to solutions of the identification problem. Moreover, we derive an error bound and corresponding convergence rates provided a suitable range-type source condition is satisfied. For the numerical solution we propose a conjugate gradient method. To illustrate the theoretical results, a numerical case study is presented which supports our analytical findings.