E. Kuhl

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.

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.

Discovering the mechanics of artificial and real meat

This paper compares the mechanical properties of artificial and real meat using tension, compression, and shear tests, finding that artificial chicken matches real chicken in stress values but tofurky is stiffer and less shear-resistant.

Automated model discovery for muscle using constitutive recurrent neural networks

This paper introduces a constitutive recurrent neural network to model viscoelastic behavior of skeletal muscle, outperforming traditional models and vanilla neural networks in prediction accuracy.

Automated model discovery for human brain using Constitutive Artificial Neural Networks

This paper introduces Constitutive Artificial Neural Networks to automatically discover both constitutive models and their parameters for human brain tissue from experimental data.

Principal-stretch-based constitutive neural networks autonomously discover a subclass of Ogden models for human brain tissue

This paper introduces a neural network that automatically discovers constitutive models for human brain tissue, outperforming traditional models in capturing tension, compression, and shear behavior.

Automated model discovery for skin:Discovering the best model, data, and experiment

This paper introduces Constitutive Artificial Neural Networks (CANN) to automatically discover the best constitutive model, parameters, and experiments for skin mechanics.

Predicting brain atrophy from tau pathology: A summary of clinical findings and their translation into personalized models

The paper develops personalized tau-atrophy models using clinical data to estimate brain atrophy rates linked to tau pathology in Alzheimer's disease.

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.

Classifying drugs by their arrhythmogenic risk using machine learning

This paper uses machine learning to classify drugs by their arrhythmogenic risk based on their effects on two key ion currents.