Gerhard A. Holzapfel

This study develops and validates two fluid-structure-growth models to predict long-term outcomes of vascular grafts in endogenous tissue restoration.

This study compares the mechanical behavior of synthetic brain phantoms and porcine brain tissue at different strain rates, identifying limitations in current materials and suggesting improvements.

This study introduces a mechanical method to quantify free-water fraction in macroporous hydrogels and demonstrates its application in characterizing fluid mobility under deformation.

This study assesses inter-laboratory variability in mechanical property characterization of porcine aorta through two testing rounds, highlighting persistent discrepancies despite protocol standardization.

This study evaluates the robustness of automated model discovery in human myocardial tissue by incorporating generalized structure tensors to account for fiber dispersion.

This paper introduces Bayesian constitutive neural networks to model soft matter systems, showing they can discover interpretable models, quantify uncertainties, and distinguish between healthy and diseased arteries.

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

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