Model Validation and Uncertainty Quantification in Biomechanics

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-Schrödinger 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 Schrödinger 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.

• Part 1. Backgrounds and Fundamentals1. Modeling the fundamental biomechanical systems. from the cardiovascular system to the brain2. Uncertainty quantification. From standard approaches to Bayes' theorem3. Sensitivity analysis4. Machine learning and surrogate modeling5. Model validation. Current state-of-the-art approachesPart 2. Model Validation6. Validation of computational fluid dynamics to 4D-flow MRI7. Model validation in skin simulations8. Validation of thrombus formation models in cardiovascular applications9. Model validation in brain simulations10. Mouse-based experiments for model validations11. Model validation of cardiac simulations12. Validation of finite-element simulations on the deployment and migration of stent-grafts in the aorta13. Model validation in lung simulationsPart 3. Uncertainty Quantification14. Model validation and uncertainty quantification of aortic valve simulations15. Sensitivity and uncertainty quantification in vascular modeling16. Uncertainty quantification and sensitivity analysis for cardiovascular models in healthy and dissected states17. Describing geometrical uncertainties with statistical shape models18. Bayesian uncertainty quantification with multi-fidelity data and Gaussian processes for impedance cardiography of aortic diseases19. Capturing the mechanical response with a hierarchical Bayesian framework in wound healing20. Hemodynamics in aortic type B dissection with the focus of sensitivity and dimensional analysis21. Vascular models and related uncertainties in computational medicine. tools for capturing patient-specificity and variability22. A Bayesian approach to describe uncertainties in Windkessel parameters in patient-specific aortic dissectionPart 4. Uncertainty Quantification with Machine Learning23. Predictability of artificial neural networks in constitutive modeling on brain tissue24. Neural networks as a tool for uncertainty quantification25. Data-driven generation of 4D velocity profiles in the ascending aorta26. A deep-learning-augmented model for real-time prediction of fractional flow reserve27. Uncertainties in image segmentation and automatic segmentation based on artificial intelligence