Soft structures are capable of undergoing reversible large strains and deformations when
facing different types of loadings. Due to the limitations of linear elastic models, researchers …

The mechanical response of a homogeneous isotropic linearly elastic material can be fully
characterized by two physical constants, the Young's modulus and the Poisson's ratio, which …

In the present work, two machine learning based constitutive models for finite deformations
are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic …

Data-driven methods are becoming an essential part of computational mechanics due to
their advantages over traditional material modeling. Deep neural networks are able to learn …

Soft biological tissues are complex materials with a large structural variety, with differences
in behavior, but with some common characteristics. Skin is an archetypal soft tissue which …

Understanding the mechanisms of deformation of biological materials is important for
improved diagnosis and therapy, fundamental investigations in mechanobiology, and …

We review twelve invariant and dispersion-type anisotropic hyperelastic constitutive models
for soft biological tissues based on their fitting performance to various experimental data. To …

Wrinkling of thin films under tension is omnipresent in nature and modern industry, a
phenomenon which has aroused considerable attention during the past two decades …

In this work, we present a novel anisotropic data-driven hyperelasticity framework for the
constitutive modeling of soft biological tissues that allows direct incorporation of …

Algorithm 5 Update Damage (Explicit Only) 1: for each particle, p, do 2://Compute geometric
resistance: Dn c 3: Dn c= dn p− l2 0∆ dn p 4://Compute driving force:(1− dn p) Dn p 5: Get un …