Diffuse interface methods for inverse problems: case study for an elliptic Cauchy problem. (4th November 2015)

• Record Type: Journal Article
• Authors: Burger, Martin; Elvetun, Ole Løseth; Schlottbom, Matthias
• Is Part Of: Inverse problems. Volume 31:Number 12(2015:Dec.)
• Publication Date: 2015-11-04
• Subjects: diffuse domain method -- ECG inversion -- inverse problems -- variational regularization -- Cauchy problem -- convergence analysis; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/0266-5611/31/12/125002
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: Many inverse problems have to deal with complex, evolving and often not exactly known geometries, e.g. as domains of forward problems modeled by partial differential equations. This makes it desirable to use methods which are robust with respect to perturbed or not well resolved domains, and which allow for efficient discretizations not resolving any fine detail of those geometries. For forward problems in partial differential equations methods based on diffuse interface representations have gained strong attention in the last years, but so far they have not been considered systematically for inverse problems. In this work we introduce a diffuse domain method as a tool for the solution of variational inverse problems. As a particular example we study ECG inversion in further detail. ECG inversion is a linear inverse source problem with boundary measurements governed by an anisotropic diffusion equation, which naturally cries for solutions under changing geometries, namely the beating heart. We formulate a regularization strategy using Tikhonov regularization and, using standard source conditions, we prove convergence rates. A special property of our approach is that not only operator perturbations are introduced by the diffuse domain method, but more important we have to deal with topologies which depend on a parameter in the diffuse domain method, i.e. we have to deal with -dependent forward operators and -dependent norms. In particular the appropriate function spaces for the unknown and the data depend on This prevents the application of some standard convergence techniques for inverse problems, in particular interpreting the perturbations as data errors in the original problem does not yield suitable results. We consequently develop a novel approach based on saddle-point problems. The numerical solution of the problem is discussed as well and results for several computational experiments are reported. In particular investigations of convergence rates support our theoretical findings.