Reduced order models for the biomechanics of living organs
著者
書誌事項
Reduced order models for the biomechanics of living organs
(Biomechanics of living organs / series editors, Jacques Ohayon and Yohan Payan)
Academic Press, c2023
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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
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