An efficient approach for improving the predictive understanding of dynamic mechanical
system variability is developed in this work. The approach requires low model assessment …

This work addresses an efficient neural network (NN) representation for the phase-field
modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural …

Multiscale computational solid mechanics concurrently connects complex material physics
and macroscopic structural analysis to accelerate the application of advanced materials in …

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 …

We extend the model-free Data-Driven computing paradigm to solids and structures that are
stochastic due to intrinsic randomness in the material behavior. The behavior of such …

Ultimate limit state (ULS) assessment examines the maximum load-carrying capacity of
structures considering inelastic buckling failure. Contrary to the traditional allowable stress …

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 …

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 …

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 …