TY  - BOOK
AU  - Dokken,Jørgen S.
AU  - Mardal,Kent-Andre
AU  - Rognes,Marie E.
AU  - Valnes,Lars Magnus
AU  - Vinje,Vegard
ED  - SpringerLink (Online service)
TI  - Mathematical Modelling of the Human Brain II: From Glymphatics to Deep Learning 
T2  - Simula SpringerBriefs on Computing,
SN  - 9783032006790
AV  - QA71-90 
U1  - 518 23
PY  - 2026///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Mathematics
KW  - Data processing
KW  - Neural networks (Computer science) 
KW  - Biomathematics
KW  - Computational neuroscience
KW  - Nervous system
KW  - Surgery
KW  - Mathematical physics
KW  - Computer simulation
KW  - Computational Mathematics and Numerical Analysis
KW  - Mathematical Models of Cognitive Processes and Neural Networks
KW  - Mathematical and Computational Biology
KW  - Computational Neuroscience
KW  - Neurosurgery
KW  - Computational Physics and Simulations
N1  - 1 From brain physiology to brain physics -- 2 Meshing the intracranial compartments: Cerebellum, cerebrum, brainstem and cerebrospinal fluid -- 3 Segmenting, meshing and modeling CSF spaces -- 4 The pulsating brain: An interface-coupled fluid-poroelasticinteraction model of the cranial cavity -- 5 Quantifying cerebrospinal fluid tracer concentration in the brain -- 6 Signal increase ratio prediction with CNNs -- 7 Estimating molecular transport parameters using inverse PDEmodels -- 8 Two-compartment modeling of tracer transport in the brain -- 9 An introduction to identifying velocity fields from contrast imaging via PDE-constrained optimization. 10 An introduction to network models of neurodegenerative diseases; Open Access
N2  - This open access book revolves around predictive mathematical modelling and simulation of brain multiphysics with an emphasis on cerebrospinal fluid flow and solute transport in and around the human brain. The book consists of 10 self-contained and relatively short chapters, each offering a rapid introduction to a key problem or topic, supported by open source software. Readers will gain insights into state-of-the-art mathematical tools and techniques for modelling and simulation of brain multiphysics ranging from classical finite element approaches, network-based modelling techniques and deep neural networks. The target audiences are researchers in applied mathematics, scientific computing, biophysics, bioengineering or computational neuroscience interested in a compact introduction to image-based computational modeling of brain multiphysics and cutting-edge available tools
UR  - https://doi.org/10.1007/978-3-032-00679-0
ER  -