On the optimization of initial conditions for a model parameter estimation

Matonoha, Ctirad, Papáček, Štěpán, and Kindermann, Stefan. "On the optimization of initial conditions for a model parameter estimation." Programs and Algorithms of Numerical Mathematics. Prague: Institute of Mathematics CAS, 2017. 73-80. <http://eudml.org/doc/288160>.

@inProceedings{Matonoha2017,
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.},
author = {Matonoha, Ctirad, Papáček, Štěpán, Kindermann, Stefan},
booktitle = {Programs and Algorithms of Numerical Mathematics},
keywords = {FRAP; sensitivity analysis; optimal experimental design; parameter estimation; finite differences},
location = {Prague},
pages = {73-80},
publisher = {Institute of Mathematics CAS},
title = {On the optimization of initial conditions for a model parameter estimation},
url = {http://eudml.org/doc/288160},
year = {2017},
}

TY - CLSWK
AU - Matonoha, Ctirad
AU - Papáček, Štěpán
AU - Kindermann, Stefan
TI - On the optimization of initial conditions for a model parameter estimation
T2 - Programs and Algorithms of Numerical Mathematics
PY - 2017
CY - Prague
PB - Institute of Mathematics CAS
SP - 73
EP - 80
AB - 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.
KW - FRAP; sensitivity analysis; optimal experimental design; parameter estimation; finite differences
UR - http://eudml.org/doc/288160
ER -