Patient-specific cardiovascular simulation has become a paradigm in cardiovascular
research and is emerging as a powerful tool in basic, translational and clinical research. In …

Abstract Machine learning is increasingly recognized as a promising technology in the
biological, biomedical, and behavioral sciences. There can be no argument that this …

In many applications it is important to estimate a fluid flow field from limited and possibly
corrupt measurements. Current methods in flow estimation often use least squares …

Reduced-order models based on physics are a popular choice in cardiovascular modeling
due to their efficiency, but they may experience loss in accuracy when working with …

Implementation of boundary conditions in cardiovascular simulations poses numerical
challenges due to the complex dynamic behavior of the circulatory system. The use of …

Numerous studies have suggested that medical image derived computational mechanics
models could be developed to reduce mortality and morbidity due to cardiovascular …

The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to
the challenging field of virtual design and testing of technologies in a patient-specific …

Computational fluid dynamic methods are currently being used clinically to simulate blood
flow and pressure and predict the functional significance of atherosclerotic lesions in patient …

Cardiovascular (CV) disease impacts tens of millions of people annually and carries a
massive global economic burden. Continued advances in medical imaging, hardware and …

Cardiovascular disease is a deadly global health crisis that carries a substantial financial
burden. Innovative treatment and management of cardiovascular disease straddles …