The research group develops mechanistic and data-driven models of the cardiovascular system with the aim of enabling patient-specific planning and optimization of treatment decisions. The focus is on the numerical modeling of cardiac and vascular hemodynamics, including interactions with implants and pharmacological interventions.

For clinical application, what-if simulation environments are being developed in which interventions can be virtually tested and optimized on individual patients before being performed. This concept culminates in the Digital Heart Twin Berlin—a patient-specific modeling platform for treatment planning and decision support.

This should be distinguished from the use of virtual cohorts: These are not intended for individual treatment decisions, but rather for the development, optimization, and safety-related evaluation of medical devices and therapeutic strategies. Population-based in silico studies make it possible to systematically analyze design decisions, performance limits, and potential risks as early as the preclinical phase.

Methodologically, we combine classical mechanistic modeling with modern artificial intelligence. While data-driven methods identify patterns and support predictions, mechanistic models provide explainable relationships and answer key clinical questions: Which therapy makes sense for this patient—and why?

In this way, we bridge the gap between simulation, product development, clinical decision support, and precision medicine.

• Einstein Center Digital Future
• DFG SPP 2311
• DFG SPP 2353
• Publications