The concept of multiscale modelling has emerged over the last few decades to describe
procedures that seek to simulate continuum-scale behaviour using information gleaned from …

Since the beginning of the industrial age, material performance and design have been in the
midst of innovation of many disruptive technologies. Today's electronics, space, medical …

In the present work, two machine learning based constitutive models for finite deformations
are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic …

A new data-driven computational framework is developed to assist in the design and
modeling of new material systems and structures. The proposed framework integrates three …

Composites are widely used to create tunable materials to achieve superior mechanical
properties. Brittle materials fail catastrophically in the presence of cracks. Incorporating …

We present a deep energy method for finite deformation hyperelasticitiy using deep neural
networks (DNNs). The method avoids entirely a discretization such as FEM. Instead, the …

Abstract An artificial Neural Network (NNW) is designed to serve as a surrogate model of
micro-scale simulations in the context of multi-scale analyses in solid mechanics. The …

Many geological materials, such as shale, mudstone, carbonate rock, limestone and rock
salt are multi-porosity porous media in which pores of different scales may co-exist in the …

The discovery of efficient and accurate descriptions for the macroscopic behavior of
materials with complex microstructure is an outstanding challenge in mechanics of …

In this paper, a new data-driven multiscale material modeling method, which we refer to as
deep material network, is developed based on mechanistic homogenization theory of …