The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

In the present work, a hyperelastic constitutive model based on neural networks is proposed
which fulfills all common constitutive conditions by construction, and in particular, is …

We extend the scope of our recently developed approach for unsupervised automated
discovery of material laws (denoted as EUCLID) to the general case of a material belonging …

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) …

We present a framework for the multiscale modeling of finite strain magneto-elasticity based
on physics-augmented neural networks (NNs). By using a set of problem specific invariants …

Data-driven constitutive modeling with neural networks has received increased interest in
recent years due to its ability to easily incorporate physical and mechanistic constraints and …

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 …

The constitutive behavior of polymeric materials is often modeled by finite linear viscoelastic
(FLV) or quasi-linear viscoelastic (QLV) models. These popular models are simplifications …

We present neural network-based constitutive models for hyperelastic geometrically exact
beams. The proposed models are physics-augmented, ie, formulated to fulfill important …

In this work, an experimentally validated multiscale modeling framework for additively
manufactured shell lattice structures with graded parameters is introduced. It is exemplified …