Atrial constitutive neural networks

Publikationstyp
Conference Paper
Date Issued
2025-06
Sprache
English
Author(s)
Peirlinck, Mathias  
Linka, Kevin  
Kuhl, Ellen  
TORE-URI
https://hdl.handle.net/11420/58923
First published in
Lecture notes in computer science  
Number in series
15672
Start Page
249
End Page
259
Citation
13th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2025
Contribution to Conference
13th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2025  
Publisher DOI
10.1007/978-3-031-94559-5_23
Publisher
Springer
ISBN of container
978-3-031-94559-5
978-3-031-94558-8
This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective
on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health.
Subjects
atrial mechanics
material model discovery
constitutive modeling
constitutive neural networks
DDC Class
620.1: Engineering Mechanics and Materials Science