The environmental impact of CO2 emissions is widely acknowledged, making the
development of alternative propulsion systems a priority. Hydrogen is a potential candidate …

Composite materials and structures are inherently inhomogeneous and anisotropic across
multiple scales. Multiscale modelling offers opportunities to understand the coupling of …

In the past few decades, the development of aircraft engines has targeted high bypass ratios
and lightweight construction. The use of lighter and larger fan blades can facilitate the …

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 …

Fibre-reinforced polymer (FRP) composites have been widely used in different engineering
sectors due to their excellent physical and mechanical properties. Therefore, fast …

Despite advancements in predicting the constitutive relationships of composite materials,
characterizing the effects of microstructural randomness on their mechanical behaviors …

In this paper, a data-driven multiscale finite-element method (data-driven FE2) is proposed
using a deep neural network (DNN) and proper orthogonal decomposition (POD) to …

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 …

The distance-minimizing data-driven computational mechanics has great potential in
engineering applications by eliminating material modeling error and uncertainty. In this …

With an emphasis on the development of machine learning-based constitutive modeling
approaches, the state of constitutive modeling techniques and applications for metals and …