Mathematical Modelling of the Human Brain II : From Glymphatics to Deep Learning / / edited by Jørgen S. Dokken, Kent-Andre Mardal, Marie E. Rognes, Lars Magnus Valnes, Vegard Vinje

• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026
• Edizione: 1st ed. 2026.
• Descrizione fisica: 1 online resource (XIII, 150 p. 50 illus., 44 illus. in color.)
• Disciplina: 518
• Soggetto topico: Mathematics - Data processing; Neural networks (Computer science); Biomathematics; Computational neuroscience; Nervous system - Surgery; Mathematical physics; Computer simulation; Computational Mathematics and Numerical Analysis; Mathematical Models of Cognitive Processes and Neural Networks; Mathematical and Computational Biology; Computational Neuroscience; Neurosurgery; Computational Physics and Simulations
• Persona (resp. second.): DokkenJørgen S; MardalKent-Andre; RognesMarie E; ValnesLars Magnus; VinjeVegard
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Mathematical Modelling of the Human Brain II
• ISBN: 3-032-00679-1; 9783032006790
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911046524903321
• Lo trovi qui: Univ. Federico II
• Serie: Simula SpringerBriefs on Computing, . 2512-1685 ; ; 18