Reduced Order Models for the Biomechanics of Living Organs (eBook, ePUB)

Redaktion: Chinesta, Francisco; Ohayon, Jacques; Payan, Yohan; Cueto, Elias

• Format: ePub

Produktbeschreibung

Reduced Order Models for the Biomechanics of Living Organs, a new volume in the Biomechanics of Living Organisms series, provides a comprehensive overview of the state-of-the-art in biomechanical computations using reduced order models, along with a deeper understanding of the associated reduction algorithms that will face students, researchers, clinicians and industrial partners in the future. The book gathers perspectives from key opinion scientists who describe and detail their approaches, methodologies and findings. It is the first to synthesize complementary advances in Biomechanical modelling of living organs using reduced order techniques in the design of medical devices and clinical interventions, including surgical procedures.

This book provides an opportunity for students, researchers, clinicians and engineers to study the main topics related to biomechanics and reduced models in a single reference, with this volume summarizing all biomechanical aspects of each living organ in one comprehensive reference.

• Introduces the fundamental aspects of reduced order models
• Presents the main computational studies in the field of solid and fluid biomechanical modeling of living organs
• Explores the use of reduced order models in the fields of biomechanical electrophysiology, tissue growth and prosthetic designs

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 492
• Erscheinungstermin: 25. Mai 2023
• Englisch
• ISBN-13: 9780323915762
• Artikelnr.: 68129673

Inhaltsangabe

Part 1: Backgrounds and Fundamentals of Reduced Order Models 1. An
introduction to Model Order Reduction Techniques 2. Linear and nonlinear
dimensionality reduction of biomechanical models 3. Shape parameterizations
for reduced order modeling in biophysics 4. Data-driven modelling and
artificial intelligence 5. Deep Learning for Real-Time Computational
Biomechanics 6. An introduction to Pod-Greedy-Galerkin reduced basis method
7. Machine learning and biophysical models: how to benefit each other?
Part 2: Applications to Computational Fluid Biomechanics 8. Fast and
accurate numerical simulations for the study of coronary artery  bypass
grafts  by artificial neural network 9. Reduced Order Models for Fluid
inside Aneurysms using Proper Orthogonal Decomposition 10. Isogeometric
Hierarchical Model Reduction for advection-diffusion process simulation in
microchannels 11. Fast closed-loop CFD model for patient-specific aortic
dissection management 12. Reduced order modelling for direct and inverse
problems in haemodynamics
Part 3: Applications to Computational Solid Biomechanics and living tissues
13. Model Order Reduction of a 3D biome-chanical tongue model: a necessary
step for quantitative evaluation of models of speech motor control and
planning 14. Deep learning contributions for reducing the complexity of
prostate biomechanical models 15. Reduced Mechanical model of trunk-lumbar
belt interaction for design-oriented in-silico clinical trials 16.
ROM-based patient-specific structural analysis of vertebrae affected by
metastasis 17. Reduced Order Models for Prediction of Successful Course of
Vaginal Delivery 18. Modeling and simulation of a realistic knee joint
using biphasic materials by the means of the proper generalized
decomposition 19. Comparison of three machine learning methods to estimate
myocardial stiffness
Part 4: Applications to Biomechanical Electrophysiology, Image processing
and Surgical protocols 20. Real-time numerical prediction of strain
localization using dictionary-based ROM-nets for sitting-acquired deep
tissue injury prevention 21. Reduced order modeling of the cardiac function
across the scales 22. Surgery simulators based on model order reduction