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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Illustrationen
Approx. 250 illustrations (250 in full color)
Dateigröße
ISBN-13
978-0-323-91576-2 (9780323915762)
Schweitzer Klassifikation
Part 1: Backgrounds and Fundamentals of Reduced Order Models1. An introduction to Model Order Reduction Techniques2. Linear and nonlinear dimensionality reduction of biomechanical models3. Shape parameterizations for reduced order modeling in biophysics4. Data-driven modelling and artificial intelligence5. Deep Learning for Real-Time Computational Biomechanics6. An introduction to Pod-Greedy-Galerkin reduced basis method7. Machine learning and biophysical models: how to benefit each other?
Part 2: Applications to Computational Fluid Biomechanics8. Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural network9. Reduced Order Models for Fluid inside Aneurysms using Proper Orthogonal Decomposition10. Isogeometric Hierarchical Model Reduction for advection-diffusion process simulation in microchannels11. Fast closed-loop CFD model for patient-specific aortic dissection management12. Reduced order modelling for direct and inverse problems in haemodynamics
Part 3: Applications to Computational Solid Biomechanics and living tissues13. Model Order Reduction of a 3D biome-chanical tongue model: a necessary step for quantitative evaluation of models of speech motor control and planning14. Deep learning contributions for reducing the complexity of prostate biomechanical models15. Reduced Mechanical model of trunk-lumbar belt interaction for design-oriented in-silico clinical trials16. ROM-based patient-specific structural analysis of vertebrae affected by metastasis17. Reduced Order Models for Prediction of Successful Course of Vaginal Delivery18. Modeling and simulation of a realistic knee joint using biphasic materials by the means of the proper generalized decomposition19. Comparison of three machine learning methods to estimate myocardial stiffness
Part 4: Applications to Biomechanical Electrophysiology, Image processing and Surgical protocols20. Real-time numerical prediction of strain localization using dictionary-based ROM-nets for sitting-acquired deep tissue injury prevention21. Reduced order modeling of the cardiac function across the scales22. Surgery simulators based on model order reduction
