The recent decades have seen various attempts at accelerating the process of developing
materials targeted towards specific applications. The performance required for a particular …

Computational multiscale methods for analyzing and deriving constitutive responses have
been used as a tool in engineering problems because of their ability to combine information …

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 …

Composite plates are widely used in many engineering fields such as aerospace and
automotive. An accurate and efficient multiscale modeling and simulation strategy is of …

We present a machine learning framework capable of consistently inferring mathematical
expressions of hyperelastic energy functionals for incompressible materials from sparse …

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 …

This paper aims to propose a data-driven computing algorithm integrated with model
reduction technique to conduct instability analysis of thin composite structures. The data …

Abstract Data-driven Computational Mechanics (DDCM) has been proposed as a new
computational paradigm in recent years. Most of the DDCM models are discretised in the …

Phenomenological constitutive modeling is prone to uncertainty and results in loss of
information as data coming from experiments are not used directly in calculations. Data …

In this paper, a data-driven enhanced combined finite-discrete element method (DDFDEM)
is proposed to simulate the rock mechanical behavior by directly assigning the rock …