Herein, we present a new data-driven multiscale framework called FE ANN which is based
on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) …

Data-driven methods have emerged as a promising framework for material constitutive
modeling. However, traditional data-driven models are hindered by limitations arising from a …

The stiffness of soft biological tissues not only depends on the applied deformation, but also
on the deformation rate. To model this type of behavior, traditional approaches select a …

This contribution discusses surrogate models that emulate the solution field (s) in the entire
simulation domain. The surrogate uses the most characteristic modes of the solution field (s) …

The mathematical formulation of constitutive models to describe the path‐dependent, that is,
inelastic, behavior of materials is a challenging task and has been a focus in mechanics …

Conventional neural network elastoplasticity models are often perceived as lacking
interpretability. This paper introduces a two-step machine learning approach that returns …

Numerical modeling of localizations is a challenging task due to the evolving rough solution
in which the localization paths are not predefined. Despite decades of efforts, there is a need …

Mechanics‐specific recurrent neural network (RNN) models are known for their ability to
describe the complex three‐dimensional stress–strain response of elasto‐plastic solids for …

Short fiber–reinforced composites (SFRCs) are high-performance engineering materials for
lightweight structural applications in the automotive and electronics industries. Typically …

This paper introduces a neural kernel method to generate machine learning plasticity
models for micropolar and micromorphic materials that lack material symmetry and have …