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

Author:   Nikolas Uesseler
Publisher:   Springer Fachmedien Wiesbaden
ISBN:  

9783658503437


Pages:   76
Publication Date:   30 January 2026
Format:   Paperback
Availability:   Not yet available   Availability explained
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Parameter Identification for a Stochastic Partial Differential Equation in the Nonstationary Case


Overview

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.

Full Product Details

Author:   Nikolas Uesseler
Publisher:   Springer Fachmedien Wiesbaden
Imprint:   Springer Spektrum
ISBN:  

9783658503437


ISBN 10:   3658503432
Pages:   76
Publication Date:   30 January 2026
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

Tikhonov regularization in Nonlinear Problems.- On the Conditions for Convergence Rates.- The Generalized Tikhonov Functional.- The Tools to Work with Random Data.- Application: Parameter Identification of SDEs.

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Author Information

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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