Machine learning tools represent key enablers for empowering material scientists and
engineers to accelerate the development of novel materials, processes and techniques. One …

This study offered a detailed review of data sciences and machine learning (ML) roles in
different petroleum engineering and geosciences segments such as petroleum exploration …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

Mechanical discontinuity in the material is generally referred as crack or fracture. Predicting
and monitoring the geometry, distribution, and condition of mechanical discontinuities are …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

A digital twin can be defined as an adaptive model of a complex physical system. Recent
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …

Architected materials typically rely on regular periodic patterns to achieve improved
mechanical properties such as stiffness or fracture toughness. Here we introduce a class of …

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

We propose a machine learning approach to address a key challenge in materials science:
predicting how fractures propagate in brittle materials under stress, and how these materials …

Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of
cracks aided by high internal stresses. Hence, accurate prediction of maximum internal …