Model Validation and Uncertainty Quantification in Biomechanics : From Soft Biological Tissue to Blood Flow
Book Details
Format
Hardback or Cased Book
Book Series
Biomechanics of Living Organs
ISBN-10
0443330166
ISBN-13
9780443330162
Publisher
Elsevier Science Publishing Co Inc
Imprint
Academic Press Inc
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Aug 1st, 2026
Print length
400 Pages
Weight
450 grams
Product Classification:
Biomedical engineeringBiomedical engineering / Medical engineeringBiotechnology
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Model Validation and Uncertainty Quantification in Biomechanics: From Soft Biological Tissue to Blood Flow provides a comprehensive overview of the latest technology in biomechanical modeling and analysis. Part I presents the foundational principles of modeling primary biomechanical systems, including the intricate workings of the cardiovascular system. This section also provides invaluable insights into essential topics such as sensitivity analysis, uncertainty quantification, machine learning, and surrogate modeling. In Part 2, the book transitions into an in-depth examination of the current state-of-the-art in model validation techniques across a diverse array of biomechanical disciplines, including the latest advancements and best practices.
Part 3 introduces current and innovative approaches for quantifying uncertainties inherent in biomechanical modeling. Chapters range from established methodologies to emerging techniques, providing a comprehensive overview of the various strategies employed in addressing uncertainty in biomechanical studies. Finally, in Part 4, the book concludes with a focus on cutting-edge methods, specifically spotlighting the utilization of machine learning and surrogate modeling for both model validation and uncertainty quantification. Through real-world applications and case studies, this book provides an in-depth understanding of how these advanced techniques are reshaping the landscape of biomechanics research.
Part 3 introduces current and innovative approaches for quantifying uncertainties inherent in biomechanical modeling. Chapters range from established methodologies to emerging techniques, providing a comprehensive overview of the various strategies employed in addressing uncertainty in biomechanical studies. Finally, in Part 4, the book concludes with a focus on cutting-edge methods, specifically spotlighting the utilization of machine learning and surrogate modeling for both model validation and uncertainty quantification. Through real-world applications and case studies, this book provides an in-depth understanding of how these advanced techniques are reshaping the landscape of biomechanics research.
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