Identification of discontinuous coefficients in elliptic problems using total variation regularization

We propose several formulations for recovering discontinuous coefficients in elliptic problems by using total variation (TV) regularization. The motivation for using TV is its well-established ability to recover sharp discontinuities. We employ an augmented Lagrangian variational formulation for sol...

Full description

Saved in:
Bibliographic Details
Main Authors: Chan, Tony F., Tai, Xue Cheng
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2009
Subjects:
Online Access:https://hdl.handle.net/10356/102806
http://hdl.handle.net/10220/4605
http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:ISI_WOS_XML&id=doi:&genre=&isbn=&issn=1064-8275&date=2003&volume=25&issue=3&spage=881&epage=904&aulast=Chan&aufirst=%20TF&auinit=TF&title=SIAM%20JOURNAL%20ON%20SCIENTIFIC%20COMPUTING&atitle=Identification%20of%20discontinuous%20coefficients%20in%20elliptic%20problems%20using%20total%20variation%20regularization
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:We propose several formulations for recovering discontinuous coefficients in elliptic problems by using total variation (TV) regularization. The motivation for using TV is its well-established ability to recover sharp discontinuities. We employ an augmented Lagrangian variational formulation for solving the output-least-squares inverse problem. In addition to the basic output-least-squares formulation, we introduce two new techniques for handling large observation errors. First, we use a filtering step to remove as much of the observation error as possible. Second, we introduce two extensions of the output-least-squares model; one model employs observations of the gradient of the state variable while the other uses the flux. Numerical experiments indicate that the combination of these two techniques enables us to successfully recover discontinuous coefficients even under large observation errors.