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An adaptive regularization algorithm for recovering the rate constant distribution from biosensor data
Örebro University, School of Science and Technology. Department of Engineering and Chemical Sciences, Karlstad University, Karlstad, Sweden. (Mathematics)ORCID iD: 0000-0003-4023-6352
Department of Engineering and Chemical Sciences, Karlstad University, Karlstad, Sweden.
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-0332-2315
School of Computing Science, Zhejiang University City College, Hangzhou, China.
2018 (English)In: Inverse Problems in Science and Engineering, ISSN 1741-5977, E-ISSN 1741-5985, Vol. 26, no 10, p. 1464-1489Article in journal (Refereed) Published
Abstract [en]

We present here the theoretical results and numerical analysis of a regularization method for the inverse problem of determining the rate constant distribution from biosensor data. The rate constant distribution method is a modern technique to study binding equilibrium and kinetics for chemical reactions. Finding a rate constant distribution from biosensor data can be described as a multidimensional Fredholm integral equation of the first kind, which is a typical ill-posed problem in the sense of J. Hadamard. By combining regularization theory and the goal-oriented adaptive discretization technique,we develop an Adaptive Interaction Distribution Algorithm (AIDA) for the reconstruction of rate constant distributions. The mesh refinement criteria are proposed based on the a posteriori error estimation of the finite element approximation. The stability of the obtained approximate solution with respect to data noise is proven. Finally, numerical tests for both synthetic and real data are given to show the robustness of the AIDA.

Place, publisher, year, edition, pages
Oxfordshire, United Kingdom: Taylor & Francis, 2018. Vol. 26, no 10, p. 1464-1489
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:oru:diva-64002DOI: 10.1080/17415977.2017.1411912ISI: 000438638300005Scopus ID: 2-s2.0-85037706652OAI: oai:DiVA.org:oru-64002DiVA, id: diva2:1172410
Funder
Swedish Research Council, 2015-04627
Note

Funding Agencies:

Swedish Knowledge Foundation (KKS) project HOG

AForsk Foundation

Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-08-30Bibliographically approved

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Zhang, YeGulliksson, Mårten

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CiteExportLink to record
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  • apa
  • harvard1
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  • de-DE
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Output format
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