Efficient and robust coupling methods for electro-mechanic models of the human heart

Cardiac diseases are the number one cause of death in Germany. Computational modeling of the cardiovascular system can help to understand the relevant mechanisms and help to tailor treatments for heart diseases. Although cardiac computational modeling has significantly advanced in the last decades, this is often limited to a single function such as electrophysiology, biomechanics, blood flow in the heart or the circulatory system. These single-physics approaches are very valuable research tools, but the interaction of cardiac functions is required to fully leverage the potential of in silico approaches. This project aims to extend the limits of multi-physics simulations of the human heart by developing and evaluating robust coupling schemes for models of the cardiac electro-mechanical system with high biophysical accuracy across multiple scales and dimensions. By combining our profound interdisciplinary experience in modeling each of the cardiac physical systems separately with our preliminary work in coupling them, we are well positioned to construct and analyze an efficient and accurate parallel finite element realization of the fully coupled system. As specific goals, we will develop and provide (1) a formulation for a robustly coupled model for the whole heart comprising active electrophysiology, cardiac mechanics and circulatory function; (2) an efficient and scalable numerical implementation of this model and a comprehensive convergence analysis of the approximations in space and time; (3) a user-friendly integration of the fully coupled model into the established openCARP simulation framework to foster community adoption of this research tool and pave the way for clinical translation. The numerical implementation of this coupled electro-mechanical model within the openCARP ecosystem will be a valuable complementary research tool in the armamentarium to tackle complex cardiovascular diseases. Our community building and open science efforts together with a translational proof-of-concept application in a medically relevant scenario will foster future clinical adoption of in silico methods.