Reduced Order Models for the Biomechanics of Living Organs

Francisco Chinesta, Elias Cueto, Yohan Payan, Jacques Ohayon

• Publisher: Academic Press
• Year: 2023
• Print ISBN: 9780323899673
• eBook ISBN: 9780323915762
• Topic: Technology & Engineering
• Subtopic: Engineering General
• Index: Technology & Engineering

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

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contents
• Contributors
• Editor's biography
• Foreword
• Preface
• Note of the series editors
• List of Illustrations
• List of Tables
• Chapter 1 : An introduction to model order reduction techniques
• Chapter 2 : Linear and nonlinear dimensionality reduction of biomechanical models
• Chapter 3 : Shape parameterizations for reduced order modeling in biophysics
• Chapter 4 : Data-driven modelling and artificial intelligence
• Chapter 5 : Deep learning for real-time computational biomechanics
• Chapter 6 : An introduction to POD-greedy-Galerkin reduced basis method
• Chapter 7 : Machine learning and biophysical models: how to benefit each other?
• Chapter 8 : Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural networks
• Chapter 9 : Reduced order models for fluid inside aneurysms using proper orthogonal decomposition
• Chapter 10 : Isogeometric hierarchical model reduction for advection–diffusion process simulation in microchannels
• Chapter 11 : Fast closed-loop CFD model for patient-specific aortic-dissection management
• Chapter 12 : Reduced order modelling for direct and inverse problems in haemodynamics
• Chapter 13 : Model order reduction of a 3D biomechanical tongue model: a solution for real-time movement simulations to study speech motor control
• Chapter 14 : Deep learning contributions for reducing the complexity of prostate biomechanical models
• Chapter 15 : Reduced mechanical models of trunk–lumbar belt interaction for design-oriented in-silico clinical trials
• Chapter 16 : ROM-based patient-specific structural analysis of vertebrae affected by metastasis
• Chapter 17 : Reduced order model for prediction of a successful course of vaginal delivery
• Chapter 18 : Modeling and simulation of a realistic knee joint using biphasic materials by means of the proper generalized composition
• Chapter 19 : Comparison of three machine learning methods to estimate myocardial stiffness
• Chapter 20 : Real-time numerical prediction of strain localization using dictionary-based ROM-nets for sitting-acquired deep tissue injury prevention
• Chapter 21 : Reduced order modeling of the cardiac function across the scales
• Chapter 22 : Surgery simulators based on model-order reduction
• Index
• A