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.
1. Chapter 1 : An introduction to model order reduction techniques - p. Page xxi 2. Chapter 2 : Linear and nonlinear dimensionality reduction of biomechanical models - p. Page 19 3. Chapter 3 : Shape parameterizations for reduced order modeling in biophysics - p. Page 43 4. Chapter 4 : Data-driven modelling and artificial intelligence - p. Page 60 5. Chapter 5 : Deep learning for real-time computational biomechanics - p. Page 92 6. Chapter 6 : An introduction to POD-greedy-Galerkin reduced basis method - p. Page 123 7. Chapter 7 : Machine learning and biophysical models: how to benefit each other? - p. Page 142 8. Chapter 8 : Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural networks - p. Page 161 9. Chapter 9 : Reduced order models for fluid inside aneurysms using proper orthogonal decomposition - p. Page 180 10. Chapter 10 : Isogeometric hierarchical model reduction for advection–diffusion process simulation in microchannels - p. Page 193 11. Chapter 11 : Fast closed-loop CFD model for patient-specific aortic-dissection management - p. Page 210 12. Chapter 12 : Reduced order modelling for direct and inverse problems in haemodynamics - p. Page 231 13. Chapter 13 : Model order reduction of a 3D biomechanical tongue model: a solution for real-time movement simulations to study speech motor control - p. Page 246 14. Chapter 14 : Deep learning contributions for reducing the complexity of prostate biomechanical models - p. Page 269 15. Chapter 15 : Reduced mechanical models of trunk–lumbar belt interaction for design-oriented in-silico clinical trials - p. Page 288 16. Chapter 16 : ROM-based patient-specific structural analysis of vertebrae affected by metastasis - p. Page 307 17. Chapter 17 : Reduced order model for prediction of a successful course of vaginal delivery - p. Page 324 18. Chapter 18 : Modeling and simulation of a realistic knee joint using biphasic materials by means of the proper generalized composition - p. Page 346 19. Chapter 19 : Comparison of three machine learning methods to estimate myocardial stiffness - p. Page 359 20. Chapter 20 : Real-time numerical prediction of strain localization using dictionary-based ROM-nets for sitting-acquired deep tissue injury prevention - p. Page 380 21. Chapter 21 : Reduced order modeling of the cardiac function across the scales - p. Page 399 22. Chapter 22 : Surgery simulators based on model-order reduction - p. Page 429 23. Index - p. Page 450
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.
1. Chapter 1 : An introduction to model order reduction techniques - p. Page xxi 2. Chapter 2 : Linear and nonlinear dimensionality reduction of biomechanical models - p. Page 19 3. Chapter 3 : Shape parameterizations for reduced order modeling in biophysics - p. Page 43 4. Chapter 4 : Data-driven modelling and artificial intelligence - p. Page 60 5. Chapter 5 : Deep learning for real-time computational biomechanics - p. Page 92 6. Chapter 6 : An introduction to POD-greedy-Galerkin reduced basis method - p. Page 123 7. Chapter 7 : Machine learning and biophysical models: how to benefit each other? - p. Page 142 8. Chapter 8 : Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural networks - p. Page 161 9. Chapter 9 : Reduced order models for fluid inside aneurysms using proper orthogonal decomposition - p. Page 180 10. Chapter 10 : Isogeometric hierarchical model reduction for advection–diffusion process simulation in microchannels - p. Page 193 11. Chapter 11 : Fast closed-loop CFD model for patient-specific aortic-dissection management - p. Page 210 12. Chapter 12 : Reduced order modelling for direct and inverse problems in haemodynamics - p. Page 231 13. Chapter 13 : Model order reduction of a 3D biomechanical tongue model: a solution for real-time movement simulations to study speech motor control - p. Page 246 14. Chapter 14 : Deep learning contributions for reducing the complexity of prostate biomechanical models - p. Page 269 15. Chapter 15 : Reduced mechanical models of trunk–lumbar belt interaction for design-oriented in-silico clinical trials - p. Page 288 16. Chapter 16 : ROM-based patient-specific structural analysis of vertebrae affected by metastasis - p. Page 307 17. Chapter 17 : Reduced order model for prediction of a successful course of vaginal delivery - p. Page 324 18. Chapter 18 : Modeling and simulation of a realistic knee joint using biphasic materials by means of the proper generalized composition - p. Page 346 19. Chapter 19 : Comparison of three machine learning methods to estimate myocardial stiffness - p. Page 359 20. Chapter 20 : Real-time numerical prediction of strain localization using dictionary-based ROM-nets for sitting-acquired deep tissue injury prevention - p. Page 380 21. Chapter 21 : Reduced order modeling of the cardiac function across the scales - p. Page 399 22. Chapter 22 : Surgery simulators based on model-order reduction - p. Page 429 23. Index - p. Page 450
