Box constraints and weighted sparsity regularization for identifying sources in elliptic PDEs

TL;DR

This paper investigates using boundary data with box constraints and weighted sparsity regularization to accurately identify sources in elliptic PDEs, addressing challenges posed by large null spaces.

Contribution

It introduces a novel combination of box constraints and weighted sparsity regularization for source recovery in elliptic PDEs, with theoretical analysis and numerical validation.

Findings

Weighted sparsity improves source identification accuracy.

Support of the inverse is a subset of the true source support.

Method applicable to various elliptic PDE models.

Abstract

We explore the possibility for using boundary data to identify sources in elliptic PDEs. Even though the associated forward operator has a large null space, it turns out that box constraints, combined with weighted sparsity regularization, can enable rather accurate recovery of sources with constant magnitude/strength. In addition, for sources with varying strength, the support of the inverse solution will be a subset of the support of the true source. We present both an analysis of the problem and a series of numerical experiments. Our work only addresses discretized problems. The reason for introducing the weighting procedure is that standard (unweighted) sparsity regularization fails to provide adequate results for the source identification task considered in this paper. This investigation is also motivated by applications, e.g., recovering mass distributions from measurements of gravitational fields and inverse scattering. We develop the methodology and the analysis in terms of Euclidean spaces, and our results can therefore be applied to many problems. For example, the results are equally applicable to models involving the screened Poisson equation as to models using the Helmholtz equation, with both large and small wave numbers.