SYSTEM IDENTIFICATION IN TUMOR GROWTH MODELING USING SEMI-EMPIRICAL EIGENFUNCTIONS
IOLLO, Angelo
Institut National de Recherche en Informatique et en Automatique [Inria]
Institut de Mathématiques de Bordeaux [IMB]
Modélisation, contrôle et calcul [MC2]
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Institut National de Recherche en Informatique et en Automatique [Inria]
Institut de Mathématiques de Bordeaux [IMB]
Modélisation, contrôle et calcul [MC2]
IOLLO, Angelo
Institut National de Recherche en Informatique et en Automatique [Inria]
Institut de Mathématiques de Bordeaux [IMB]
Modélisation, contrôle et calcul [MC2]
Institut National de Recherche en Informatique et en Automatique [Inria]
Institut de Mathématiques de Bordeaux [IMB]
Modélisation, contrôle et calcul [MC2]
SAUT, Olivier
Institut de Mathématiques de Bordeaux [IMB]
Centre National de la Recherche Scientifique [CNRS]
Modélisation Mathématique pour l'Oncologie [MONC]
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Centre National de la Recherche Scientifique [CNRS]
Modélisation Mathématique pour l'Oncologie [MONC]
Language
en
Article de revue
This item was published in
Mathematical Models and Methods in Applied Sciences. 2012, vol. 22, n° 6
World Scientific Publishing
English Abstract
A tumor growth model based on a parametric system of partial differential equations is considered. The system corresponds to a phenomenological description of a multi-species population evolution. A velocity field taking ...Read more >
A tumor growth model based on a parametric system of partial differential equations is considered. The system corresponds to a phenomenological description of a multi-species population evolution. A velocity field taking into account the volume increase due to cellular division is introduced and the mechanical closure is provided by a Darcy-type law. The complexity of the biological phenomenon is taken into account through a set of parameters included in the model that need to be calibrated. To this end, a system identification method based on a low-dimensional representation of the solution space is introduced. We solve several idealized identification cases corresponding to typical situations where the information is scarce in time and in terms of observable fields. Finally, applications to actual clinical data are presented.Read less <
English Keywords
Tumor growth modeling
data assimilation
inverse problems
Origin
Hal imported