Sunder Neelakantan

This paper introduces a physics-informed neural network to predict myocardial mechanics from physiological measurements, showing improved stress prediction and disease stratification in pulmonary hypertension.

This study uses a 1D-FSI model to quantify how vascular stiffening and resistance contribute to increased MPA pressure in pulmonary hypertension.

This study investigates RV biomechanical remodeling in PH, finding that collagen fiber tautness and myofiber reorientation contribute to severe RV dysfunction and failure.

The paper develops a biophysical model of the lung to quantify pulmonary surfactant's effect on lung compliance by simulating respiration and surfactant dysfunction.