Quantitative predictions of natural and induced phenomena in fractured rock is one of the
great challenges in the Earth and Energy Sciences with far‐reaching economic and …

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 …

Mineral carbon storage in mafic and ultramafic rock masses has the potential to be an
effective and permanent mechanism to reduce anthropogenic CO2. Several successful pilot …

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 …

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 …

Understanding mechanistic causes of non‐Fickian transport in fractured media is important
for many hydrogeologic processes and subsurface applications. This study elucidates the …

Topological data analysis is an emerging concept of data analysis for characterizing shapes.
A state-of-the-art tool in topological data analysis is persistent homology, which is expected …

Advanced machine learning methods could be useful to obtain novel insights into some
challenging nanomechanical problems. In this work, we employed artificial neural networks …

The influence of fracture permeability and fracture network connectivity on the equivalent
permeability tensor of the fractured rock mass is investigated and compared. 70 discrete …

Typically, thousands of computationally expensive micro-scale simulations of brittle crack
propagation are needed to upscale lower length scale phenomena to the macro-continuum …