Ellen Kuhl A

The paper compares the mechanical properties of dairy and plant-based cheeses using various tests and consumer feedback, finding significant differences in texture and consumer preference.

Grow2Fold models brain folding by integrating imaging data with mechanical simulations to show differential growth drives cortical folding patterns.

This paper investigates how crank length affects cycling performance by linking rider anatomy, hip kinematics, and aerodynamics, showing shorter cranks improve aerodynamics with minimal mechanical cost.

This paper develops probabilistic strain energy models for kidney cortex and medulla using neural networks, revealing distinct mechanical properties and variability.

This study compares constitutive neural network predictions of fiber dispersion in the right ventricle with experimental data, showing that multi-axial loading improves parameter identifiability.

This study benchmarks sensory properties of dairy and dairy-free products across ten categories, finding significant gaps in mozzarella, Greek-style yogurt, and butter.

This study compares the textural properties and consumer acceptance of plant-based and conventional breaded shrimp, finding that plant-based shrimp are harder and chewier but receive comparable hedonic ratings.

This paper develops a framework to discover constitutive models of passive myocardial mechanics directly from in vivo cardiac imaging data, improving accuracy over existing models.

This study evaluates consumer acceptance of different burger formulations by combining sensory evaluation and texture analysis, finding that moistness and meatiness are key factors influencing preference.

Stanford diners (n = 116) rated soy and soy-wheat meatballs below beef-based ones on moistness, meatiness, fattiness and tastiness, yet perceived no hardness or chewiness gap despite instrumental differences.

This paper uses tofu as a model system to study the mechanics of hydrated soft solids, revealing non-linear viscoelastic behavior and introducing a water-content-based feature network for constitutive modeling.

This study characterizes the mechanical, rheological, and sensory properties of lion's mane mushroom steak, showing it has favorable texture and perceived softness compared to meat alternatives.

This study translates lab-based arterial stress-stretch data into clinically relevant pressure-diameter predictions using a fiber-reinforced model and systematic validation.

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

This study characterizes the mechanical and sensory properties of fungi-based steak, finding it to be moist, viscous, and fibrous, with unique anisotropic behavior under dynamic compression.

Constitutive artificial neural networks trained on biaxial tests of 8 ovine main pulmonary artery samples consistently discover a two-term model: exponential first-invariant plus quadratic fifth-invariant terms.

Plant-based products span stiffness 56.65 kPa (firm tofu) to 418.9 kPa (Tofurky roast), bracketing three processed animal meats; stiffness plus shear storage and loss moduli were the most consistent readouts.

Plant-based deli slices are over twice as stiff as animal deli meats under biaxial tension; stiffness tracks brittleness rated by 18 tasters (ρ = 0.857).

The paper measures the mechanical and sensory properties of plant-based and animal meats using multi-dimensional tests and neural networks, finding that some plant-based products closely mimic animal meats in texture.

This paper uses neural networks to discover two-stage tau aggregation dynamics from PET data, revealing a more accurate model than traditional reaction-diffusion approaches.

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 study compares the mechanical properties of meat and meat analogs using standardized tests, finding that modern analogs can mimic meat mechanics with proper tuning.

This paper develops a physics-based model of the elephant trunk to predict its motion from muscle activation, revealing how specific muscle contractions enable task-specific movements.

The paper personalizes a computational upper body model to improve kinematic tracking in arm movements, showing reduced tracking errors in both static and dynamic poses.

This paper uses constitutive neural networks to automatically discover anisotropic mechanical models for warp knitted fabrics, finding that models with three microstructural directions outperform classical orthotropic models.

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

This study develops a microscopic model to understand the rheological behavior of axons under stretch, showing viscoelastic properties at moderate velocities and failure at higher velocities due to damage.

The paper develops a coupled network-based model to study interactions between amyloid-beta and tau proteins in neurodegenerative diseases, revealing their cooperative role in disease propagation.

The paper models the spread of misfolded protein aggregates in the brain using nucleation-aggregation-fragmentation dynamics and network transport mechanisms.

This paper models prion-like spreading of Alzheimer's disease by integrating misfolding kinetics with a brain connectivity network derived from the Human Connectome Project, showing accurate predictions of neuropathology and simulating therapeutic strategies.