For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …

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 …

A mechanics-informed, data-driven framework for learning the constitutive law of a nonlinear
viscoelastic material from stress–strain data using deep artificial neural networks (ANNs) is …

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

We present an approach for the data-driven modeling of nonlinear viscoelastic materials at
small strains which is based on physics-augmented neural networks (NNs) and requires …

We propose an automated computational algorithm for simultaneous model selection and
parameter identification for the hyperelastic mechanical characterization of biological tissue …

We propose a physics informed, neural network-based elasto-viscoplasticity (NN-EVP)
constitutive modeling framework for predicting the flow response in metals as a function of …

In the present work, neural networks are applied to formulate parametrized hyperelastic
constitutive models. The models fulfill all common mechanical conditions of hyperelasticity …

The mathematical description of the mechanical behavior of solid materials at the continuum
scale is one of the oldest and most challenging tasks in solid mechanics and material …