Full 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. Divided into four comprehensive parts, this book offers a thorough exploration of key concepts and cutting-edge advancements in the field. 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. This section presents the latest advancements and best practices for ensuring the accuracy and reliability of computational models. 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, providing an in-depth understanding of how these advanced techniques are reshaping the landscape of biomechanics research.
Contents
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
