Publication Cover
Applicable Analysis
An International Journal
Volume 94, 2015 - Issue 5
138
Views
0
CrossRef citations to date
0
Altmetric
Articles

Approximation of the conductivity coefficient in the heat equation

&
Pages 999-1010 | Received 05 Dec 2013, Accepted 02 Apr 2014, Published online: 06 Jun 2014
 

Abstract

In this paper, we study the conductivity coefficient determination in the heat equation from observation of the lateral Dirichlet-to-Neumann map. We define a bilinear form function Qγ associated to the boundary condition and the Dirichlet-to-Neumann map, and prove that the linearized problem d Qγ is injective. Based on the idea of complex geometrical optics solutions, we give two approximations to the conductivity coefficient by using the Fourier truncation method and the mollification method. Under the a priori assumption of the conductivity, we estimate the errors between the conductivity coefficient and its approximations by setting a suitable bound of the frequency.

AMS Subject Classifications:

Acknowledgements

This work has been done during the first author’s visit to the Department of Mathematics in University of Washington. It’s a pleasure to thank Prof Gunther Uhlmann and Dr Yang Yang for many value discussion and comments. The authors are also very grateful to the anonymous referees for their valuable comments, most of which are reflected in the final version. This work was supported by the National Natural Science Foundation of China [grant number 11171054].

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,361.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.