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Wenske, Michael: Data-aware methods for the simulation of glioblastoma multiforme. 2020
Inhalt
Abstract
Zusammenfassung
Acknowledgments
List of Abbreviations
List of Figures
List of Tables
Introduction
Medical perspective
Diagnostic techniques
Treatment techniques
Mathematical forward modeling of GBM
Problems and challenges of the PDE models
Stationary modeling of GBM
Existing numerical approaches
Outline
Preliminaries
Notations
Fully anisotropic advection diffusion reaction equations
Péclet number
Variational formulation
Finite-dimensional space and grid
Matrix expressions
Matrix exponential and logarithm
Powers of a matrix
Artificial inhomogeneous diffusion
Fractional anisotropy
Manufactured solutions
Analytical solution
Analytical diffusivity field
Error definition
The MPFA-O method
The Hölder finite volume method
Problem description
Diffusivity averaging
Scalar averaging in flux discretizations
Matrix Hölder mean
Hölder finite volume method
Numerical convergence results
Inhomogeneous isotropic case (eigenvalue)
Homogeneous anisotropic case
Inhomogeneus anisotropic case (rotation)
Inhomogeneus anisotropic case (combined)
Conclusion
Stationalization
Forward tumor models
1D Fisher-Kolmogorov-Petrovsky-Piscounov equation
Myopic forward model
Haptotactic tumor model
Stationalization of Fisher-KPP equation in 1D
Stationalized tumor models
Notes on uniqueness
Numerical methods
Numerical results
1D homogeneous Fisher-KPP equation
1D myopic testcase
2D myopic diffusion model
Investigation of gradient statistics
3D comparison of forward and stationary solutions
Implementation: The DUNE-glio module
Features of DUNE-glio
Software architecture
Application to real data sets
The glioMath data set
Data preparation
Numerical results
Comparison via Péclet numbers
Discussion and outlook
Hölder finite volume method
Discussion
Outlook
Stationalization
Discussion
Outlook
Bibliography
Appendix
Appendix
Reproduction of numerical results