MS01 - Advancing Cardiac Biomechanics: Interdisciplinary Approaches in Computational Heart Modeling

Keywords: mathematics; engineering; computer science; machine learning; biophysics; medicine

Organizers:
Christoph Augustin (1,2) – christoph.augustin@medunigraz.at
Federica Caforio (1,2,3) – federica.caforio@uni-graz.at
Elias Karabelas (2,3) – elias.karabelas@uni-graz.at

Affiliations:
(1) Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
(2) BioTechMed-Graz, Graz, Austria
(3) Institute for Mathematics and Scientific Computing, Karl-Franzens-University Graz, Graz, Austria

 

Abstract:
The field of cardiac biomechanics is at the forefront of precision medicine, where computational modeling meets clinical application. This minisymposium aims to bring together young scientists and established researchers working on innovative approaches to model the human heart's complex mechanics. As we move towards the era of cardiac digital twins and in silico clinical trials, we face both challenges and opportunities in creating more accurate, efficient, and clinically relevant heart models.

 

We invite contributions that advance current cardiac modeling techniques.

Topics of interest include, but are not limited to:

  • Multiscale and multiphysics approaches to heart modeling
  • Integration of imaging data with computational models
  • AI-driven modeling and machine learning applications in cardiac biomechanics
  • Development and validation of cardiac digital twins
  • High-performance computing strategies for large-scale simulations
  • In silico trials for cardiac devices and therapies

The minisymposium will explore how recent advancements in areas such as constitutive modeling, numerical methods, and artificial intelligence are shaping the future of cardiac biomechanics. We will discuss the challenges in modeling the heart's anisotropic, nearly incompressible nature and the innovative strategies to overcome them. Particular emphasis will be placed on interdisciplinary approaches that combine expertise from mathematics, computer science, biophysics, and medicine. We aim to foster discussions on how these models can be used to gain new insights into cardiac function, predict disease progression, and optimize personalized therapies.