Deterministic and stochastic models for complex cardiovascular phenomena
Wednesday, June 16 at 11:30am (PDT)Wednesday, June 16 at 07:30pm (BST)Thursday, June 17 03:30am (KST)
Martina Bukac (University of Notre Dame, United States), Daniele Schiavazzi (University of Notre Dame, United States)
Hemodynamic modeling has become a powerful tool in the study of basic vascular function, as well as many cardiovascular diseases. Biophysically detailed vascular simulations can reveal underlying mechanisms that help explain experimental and clinical observations. As a result, there is an increasing demand for fast and efficient numerical algorithms to solve coupled multi-physics problems arising from biomedical applications. Examples include fluid-structure interaction models (e.g., valvular modeling), fluid-porous or poroelastic medium interaction models (e.g., biological tissue or tumor modeling), as well as models of transport phenomena (e.g., transport of drugs or chemicals). Moreover, creation of realistic models might be difficult in situations where only partial knowledge is available for the input processes. Examples include, but are not limited to, uncertainty in the model anatomy, physiologic boundary conditions and material properties of the cardiac and vascular tissue. In such cases, uncertainty quantification becomes an integral part of the modeling exercise. While significant progress have been achieved in recent years, hemodynamic modeling still poses significant challenges in the mathematical and computational sciences. Thus, substantial effort is allocated to the design of adaptable and uncertainty-aware numerical methods for coupled problems due to their intricate multi-physics nature, possible strong nonlinearity and presence of uncertainty. Hence, this minisymposium focuses on methodological developments and analysis of results from deterministic and stochastic cardiovascular models.