Porous media play important roles in a wide range of scientific and engineering problems.
Recently, with their increasing application in energy conversion and storage devices, such …

Structural innovation incorporating bio-inspired composites poses a fresh angle to develop
novel lightweight forms with strengthened mechanical properties, among which a must …

Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Physical and biogeochemical heterogeneity dramatically impacts fluid flow and reactive
solute transport behaviors in geological formations across scales. From micro pores to …

Multiphase flow and reactive transport are two essential physicochemical processes that
govern the effectiveness of geological carbon sequestration (GCS). The interaction and …

In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

Convolutional neural networks (CNN) are utilized to encode the relation between initial
configurations of obstacles and three fundamental quantities in porous media: porosity (φ) …

The numerical simulation and slope stability prediction are the focus of slope disaster
research. Recently, machine learning models are commonly used in the slope stability …

In the present work, 3D convolutional neural networks (CNNs) are trained to link random
heterogeneous, multiphase materials to their elastic macroscale stiffness thus replacing …