Model Validation and Uncertainty Quantification in Biomechanics
From Soft Biological Tissue to Blood Flow
Will be published approx. on 1. September 2026
Book
Hardback
400 pages
978-0-443-33016-2 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 235 mm
Width: 191 mm
Weight
450 gr
ISBN-13
978-0-443-33016-2 (9780443330162)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Gerhard A. Holzapfel is Professor of Biomechanics and Head of the Institute of Biomechanics at Graz University of Technology (TUG), Austria, since 2007. He is also Adjunct Professor at the Norwegian University of Science and Technology (NTNU), Trondheim, Norway, and Visiting Professor at the University of Glasgow, Scotland. Until 2013 he was Professor of Biomechanics at the Royal Institute of Technology (KTH) in Stockholm, Sweden, for 9 years (7 years as an Adjunct Professor). After his PhD in Mechanical Engineering in Graz he received an Erwin-Schroedinger Scholarship for foreign countries to be a Visiting Scholar at Stanford University (1993-95). He achieved his Habilitation at TU Vienna in 1996 and received a START-Award in 1997, which is the most prestigious research award in Austria for young scientists. In the following years (1998-2004) he was the Head of a research group on "Computational Biomechanics" at TUG. Among several awards and honors in the past years he is listed in "The World's Most Influential Scientific Minds: 2014" (Thomas Reuters), he received the Erwin Schroedinger Prize 2011 from the Austrian Academy of Sciences for his lifetime achievements, and he was awarded the 2021 William Prager Medal and the 2021 Warner T. Koiter Medal. Professor Holzapfel's research includes experimental and computational biomechanics and mechanobiology with an emphasis on soft biological tissues, the cardiovascular system including blood vessels in health and disease, aortic dissections, therapeutic interventions such as balloon angioplasty and stent implantation, second-harmonic imaging microscopy and medical image processing; nonlinear continuum mechanics, constitutive (multi-scale) modeling of solids at finite strains such as cross-linked actin networks, growth and remodeling, nonlinear finite element methods, fracture and material failure. Professor Holzapfel has authored a graduate textbook entitled "Nonlinear Solid Mechanics. A Continuum Approach for Engineering" (John Wiley & Sons), and co-edited seven books. He contributed chapters to 25+ other books, and published 250+ peer-reviewed journal articles. He is the co-founder and co-editor of the International Journal "Biomechanics and Modeling in Mechanobiology" (Springer-Verlag, Berlin, Heidelberg). Malte Rolf-Pissarczyk is a postdoctoral researcher at the Institute of Biomechanics at Graz University of Technology, in Austria. His research focuses primarily on material and computational modeling of aortic dissections, ranging from multi-scale material modeling to patient-specific fluid-structure interaction modeling. In addition to his primary research focus, M. Rolf-Pissarczyk actively participates in studies on standardized best practices for the application of in silicovalidation methods and the credibility assessment of in silico methods based on ASME verification and validation standards.
Xiao Yun Xu is a Professor of Biofluid Mechanics in the Department of Chemical Engineering at Imperial College London. She joined Imperial College in 1998 as a Lecturer and became a full Professor in 2009. Professor Xu's research expertise includes computational modelling of fluid flow and mass transfer in biological systems and its biomedical applications. Her pioneering work on the development of image-based computational models for blood flow in large arteries was reported by various media, including BBC online news and Science ("How the Blood Flows?, Science, Vol. 290, November 2000). Over the last 25 years, she has established and led her research group to the cutting edge of multiscale and multi-physics modelling of transport processes in biological systems, with applications ranging from evaluations of endovascular interventional procedures for the treatment of aortic diseases to understanding of drug transport in solid tumors and thrombolytic therapies. In these fields, she has published 200+ peer-reviewed journal articles. She currently serves as an Associate Editor of International Journal for Numerical Methods in Biomedical Engineering, a member of the board of consulting editors for Journal of Biomechanics, and a member of the editorial board of Medicine in Novel Technology and Device.
Xiao Yun Xu is a Professor of Biofluid Mechanics in the Department of Chemical Engineering at Imperial College London. She joined Imperial College in 1998 as a Lecturer and became a full Professor in 2009. Professor Xu's research expertise includes computational modelling of fluid flow and mass transfer in biological systems and its biomedical applications. Her pioneering work on the development of image-based computational models for blood flow in large arteries was reported by various media, including BBC online news and Science ("How the Blood Flows?, Science, Vol. 290, November 2000). Over the last 25 years, she has established and led her research group to the cutting edge of multiscale and multi-physics modelling of transport processes in biological systems, with applications ranging from evaluations of endovascular interventional procedures for the treatment of aortic diseases to understanding of drug transport in solid tumors and thrombolytic therapies. In these fields, she has published 200+ peer-reviewed journal articles. She currently serves as an Associate Editor of International Journal for Numerical Methods in Biomedical Engineering, a member of the board of consulting editors for Journal of Biomechanics, and a member of the editorial board of Medicine in Novel Technology and Device.
Editor
Professor of Biomechanics and Head of the Institute of Biomechanics at Graz University of Technology (TUG), Austria
Postdoctoral Researcher, Institute of Biomechanics, Graz University of Technology, Austria
Professor of Biofluid Mechanics, Department of Chemical Engineering, Imperial College London, UK
Content
Part 1. Backgrounds and Fundamentals
1. Modeling the fundamental biomechanical systems. from the cardiovascular system to the brain
2. Uncertainty quantification. From standard approaches to Bayes' theorem
3. Sensitivity analysis
4. Machine learning and surrogate modeling
5. Model validation. Current state-of-the-art approaches
Part 2. Model Validation
6. Validation of computational fluid dynamics to 4D-flow MRI
7. Model validation in skin simulations
8. Validation of thrombus formation models in cardiovascular applications
9. Model validation in brain simulations
10. Mouse-based experiments for model validations
11. Model validation of cardiac simulations
12. Validation of finite-element simulations on the deployment and migration of stent-grafts in the aorta
13. Model validation in lung simulations
Part 3. Uncertainty Quantification
14. Model validation and uncertainty quantification of aortic valve simulations
15. Sensitivity and uncertainty quantification in vascular modeling
16. Uncertainty quantification and sensitivity analysis for cardiovascular models in healthy and dissected states
17. Describing geometrical uncertainties with statistical shape models
18. Bayesian uncertainty quantification with multi-fidelity data and Gaussian processes for impedance cardiography of aortic diseases
19. Capturing the mechanical response with a hierarchical Bayesian framework in wound healing
20. Hemodynamics in aortic type B dissection with the focus of sensitivity and dimensional analysis
21. Vascular models and related uncertainties in computational medicine. tools for capturing patient-specificity and variability
22. A Bayesian approach to describe uncertainties in Windkessel parameters in patient-specific aortic dissection
Part 4. Uncertainty Quantification with Machine Learning
23. Predictability of artificial neural networks in constitutive modeling on brain tissue
24. Neural networks as a tool for uncertainty quantification
25. Data-driven generation of 4D velocity profiles in the ascending aorta
26. A deep-learning-augmented model for real-time prediction of fractional flow reserve
27. Uncertainties in image segmentation and automatic segmentation based on artificial intelligence
1. Modeling the fundamental biomechanical systems. from the cardiovascular system to the brain
2. Uncertainty quantification. From standard approaches to Bayes' theorem
3. Sensitivity analysis
4. Machine learning and surrogate modeling
5. Model validation. Current state-of-the-art approaches
Part 2. Model Validation
6. Validation of computational fluid dynamics to 4D-flow MRI
7. Model validation in skin simulations
8. Validation of thrombus formation models in cardiovascular applications
9. Model validation in brain simulations
10. Mouse-based experiments for model validations
11. Model validation of cardiac simulations
12. Validation of finite-element simulations on the deployment and migration of stent-grafts in the aorta
13. Model validation in lung simulations
Part 3. Uncertainty Quantification
14. Model validation and uncertainty quantification of aortic valve simulations
15. Sensitivity and uncertainty quantification in vascular modeling
16. Uncertainty quantification and sensitivity analysis for cardiovascular models in healthy and dissected states
17. Describing geometrical uncertainties with statistical shape models
18. Bayesian uncertainty quantification with multi-fidelity data and Gaussian processes for impedance cardiography of aortic diseases
19. Capturing the mechanical response with a hierarchical Bayesian framework in wound healing
20. Hemodynamics in aortic type B dissection with the focus of sensitivity and dimensional analysis
21. Vascular models and related uncertainties in computational medicine. tools for capturing patient-specificity and variability
22. A Bayesian approach to describe uncertainties in Windkessel parameters in patient-specific aortic dissection
Part 4. Uncertainty Quantification with Machine Learning
23. Predictability of artificial neural networks in constitutive modeling on brain tissue
24. Neural networks as a tool for uncertainty quantification
25. Data-driven generation of 4D velocity profiles in the ascending aorta
26. A deep-learning-augmented model for real-time prediction of fractional flow reserve
27. Uncertainties in image segmentation and automatic segmentation based on artificial intelligence