A regularization operator for source identification for elliptic PDEs

TL;DR

This paper introduces a novel regularization operator for source identification in elliptic PDEs, overcoming limitations of standard methods by accurately locating sources anywhere in the domain.

Contribution

A new regularization approach based on a novel operator is proposed, improving source localization accuracy in elliptic PDE inverse problems.

Findings

The new method outperforms standard Tikhonov regularization in source localization.

Numerical experiments demonstrate improved accuracy and robustness.

The approach is applicable to a broad class of operator equations.

Abstract

We study a source identification problem for a prototypical elliptic PDE from Dirichlet boundary data. This problem is ill-posed, and the involved forward operator has a significant nullspace. Standard Tikhonov regularization yields solutions which approach the minimum $L^2$-norm least-squares solution as the regularization parameter tends to zero. We show that this approach 'always' suggests that the unknown local source is very close to the boundary of the domain of the PDE, regardless of the position of the true local source. We propose an alternative regularization procedure, realized in terms of a novel regularization operator, which is better suited for identifying local sources positioned anywhere in the domain of the PDE. Our approach is motivated by the classical theory for Tikhonov regularization and yields a standard quadratic optimization problem. Since the new methodology is derived for an abstract operator equation, it can be applied to many other source identification problems. This paper contains several numerical experiments and an analysis of the new methodology.