Lei Shi

The paper evaluates parameter recoverability in cardiovascular LPN inference using a probabilistic neural framework, showing that accurate hemodynamic prediction does not guarantee correct parameter recovery.

The paper develops a patient-specific cardiac mechanics model using clinical data and evaluates its performance against simplified models, finding that truncation significantly affects results.

This study uses finite element modeling to analyze how uterine and cervical structural properties affect cervical mechanical loading during pregnancy, identifying key parameters influencing preterm birth risk.

The paper develops an image-based modeling pipeline to study uterine passive mechanics during late pregnancy, achieving computational savings and accurate strain predictions.

This study uses multiscale modeling to show that left atrial wall thickness significantly affects myocardial stresses and oscillatory shear, more than other factors like cavity volume or displacement.