M. Peirlinck

Automated model discovery for human cardiac tissue: Discovering the best model and parameters

This paper automates cardiac tissue model discovery by training a neural network to identify the best model and parameters for human cardiac tissue.

Democratizing biomedical simulation through automated model discovery and a universal material subroutine

This paper automates material model selection in finite element simulations using constitutive neural networks, improving robustness and accessibility of biomedical simulations.

Digital twinning of cardiac electrophysiology for congenital heart disease

The paper develops a computational pipeline to create digital twins of cardiac electrophysiology for pediatric congenital heart disease patients using BLNMs to match clinical ECGs.

On automated model discovery and a universal material subroutine

This paper automates model selection for constitutive modeling in finite element analysis, demonstrating a universal material subroutine that generalizes to multiple models and accurately predicts stress in human brain tissue.

How drugs modulate the performance of the human heart

This study uses a multiphysics framework to show how drug concentrations affect heart rate, myofiber stretch, tissue relaxation, and cardiac output in the human heart.

A Framework for Evaluating Myocardial Stiffness Using 3D-Printed Hearts

This paper develops 3D-printed heart phantoms with known stiffness to validate myocardial stiffness estimation methods using MRI and computational modeling.