Original title: On the Optimization of Initial Conditions for a Model Parameter Estimation
Authors: Matonoha, Ctirad ; Papáček, Š. ; Kindermann, S.
Document type: Papers
Conference/Event: Programs and Algorithms of Numerical Mathematics /18./, Janov nad Nisou (CZ), 20160619
Year: 2017
Language: eng
Abstract: The design of an experiment, e.g., the setting of initial conditions, strongly influences the accuracy of the process of determining model parameters from data. The key concept relies on the analysis of the sensitivity of the measured output with respect to the model parameters. Based on this approach we optimize an experimental design factor, the initial condition for an inverse problem of a model parameter estimation. Our approach, although case independent, is illustrated at the FRAP (Fluorescence Recovery After Photobleaching) experimental technique. The core idea resides in the maximization of a sensitivity measure, which depends on the initial condition. Numerical experiments show that the discretized optimal initial condition attains only two values. The number of jumps between these values is inversely proportional to the value of a diffusion coefficient D (characterizing the biophysical and numerical process). The smaller value of D is, the larger number of jumps occurs.
Keywords: finite differences; FRAP; optimal experimental design; parameter estimation; sensitivity analysis
Project no.: ED2.1.00/01.0024, LO1205 (CEP)
Funding provider: GA MŠk, GA MŠk
Host item entry: Programs and algorithms of numerical mathematics 18, ISBN 978-80-85823-67-7
Note: Související webová stránka: http://hdl.handle.net/10338.dmlcz/703000

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0272938

Permalink: http://www.nusl.cz/ntk/nusl-358346

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Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
 Record created 2017-08-02, last modified 2020-03-26

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