Physics-constrained data-driven computing is an emerging computational paradigm that
allows simulation of complex materials directly based on material database and bypass the …

The aim of this work is to investigate the coupling of data-driven (DD) computing and model-
driven (MD) computing for the analyses of engineering structures. The data-driven …

A nominal finite element solver is proposed for data-driven finite strain elasticity. It bypasses
the need for a constitutive model by considering a database of deformation gradient/first …

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 …

In this study, a deep learning framework for multiscale finite element analysis (FE 2) is
proposed. To overcome the inefficiency of the concurrent classical FE 2 method induced by …

A new method combining computational homogenization and the Artificial Neural Network
(ANN) is proposed to construct elastoplastic composites database efficiently for data-driven …

Data-driven constitutive modeling in continuum mechanics assumes that abundant material
data are available and can effectively replace the constitutive law. To this end, Kirchdoerfer …

We propose a surrogate model for two‐scale computational homogenization of elastostatics
at finite strains. The macroscopic constitutive law is made numerically available via an …

This article introduces a manifold embedding data-driven paradigm to solve small-and finite-
strain elasticity problems without a conventional constitutive law. This formulation follows the …

We present a comparison between two approaches to modelling hyperelastic material
behaviour using data. The first approach is a novel approach based on Data-driven …