Téléchargez le livre : Parameter Identification for a Stochastic Partial Differential Equation in the Nonstationary Case

Parameter Identification for a Stochastic Partial Differential Equation in the Nonstationary Case



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Springer Spektrum


Paru le : 2026-01-01



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Description

This thesis investigates the mathematical problem of parameter identification in an equation arising from the study of how cells move on an embryo during its development. The motion of the cells can be modeled as particles evolving on a two-dimensional manifold according to a stochastic differential equation. The specific focus here is on estimating the drift parameter of this equation by observing the positions of a finite number of particles at different points in time. The general approach to approximate the solution of this ill-posed problem is to minimize a Tikhonov functional based on a regularized log-likelihood.
To assess the error of this approximation, tools from the theory of ill-posed problems are required. The thesis begins with a chronological review of fundamental results in nonlinear ill-posed problems, with the aim of motivating the assumptions underlying the main result as well as the techniques employed in its analysis from a historical perspective.
Pages
76 pages
Collection
n.c
Parution
2026-01-01
Marque
Springer Spektrum
EAN papier
9783658503437
EAN PDF
9783658503444
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
1710 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
89,66 €
EAN EPUB
9783658503444
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
4465 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
89,66 €

Nikolas Uesseler is pursuing a PhD in applied mathematics at the University of Münster in the field of inverse problems and mathematical imaging in Prof. Benedikt Wirth's research group.

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