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 …

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 …

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 …

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

Fine-scale models that represent first-principles physics are challenging to represent at
larger scales of interest in many application areas. In nanoporous media such as tight-shale …

Acid fracturing is one of the most effective techniques for improving the productivity of
naturally fractured carbonate reservoirs. Natural fractures (NFs) significantly affect the …

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

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