Understanding uncertainty is critical, especially when data are sparse and variations are
large. Bayesian neural networks offer a powerful strategy to build predictable models from …

The two fundamental concepts of materials theory, pseudo potentials and the assumption of
a multiplicative decomposition, allow a general description of inelastic material behavior …

In contemporary elasticity theory, the strain–energy function predominantly relies on the first
invariant I 1 of the deformation tensor; a practice that has been influenced by models derived …

Eating less meat is associated with a healthier body and planet. Yet, we remain reluctant to
switch to a plant-based diet, largely due to the sensory experience of plant-based meat …

We present a data-driven framework for the multiscale modeling of anisotropic finite strain
elasticity based on physics-augmented neural networks (PANNs). Our approach allows the …

For more than half a century, scientists have developed mathematical models to understand
the behavior of the human heart. Today, we have dozens of heart tissue models to choose …

Textile fabrics have unique mechanical properties, which make them ideal candidates for
many engineering and medical applications: They are initially flexible, nonlinearly stiffening …

Personalized computational simulations have emerged as a vital tool to understand the
biomechanical factors of a disease, predict disease progression, and design personalized …

The class of Constitutive Artificial Neural Networks (CANNs) represents a new approach of
neural networks in the field of constitutive modeling. So far, CANNs have proven to be a …

The ability to automatically discover interpretable mathematical models from data could
forever change how we model soft matter systems. For convex discovery problems with a …