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

Pore-scale imaging and modeling has advanced greatly through the integration of Deep
Learning into the workflow, from image processing to simulating physical processes. In …

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 …

Abstract We present the PoreFlow-Net, a 3D convolutional neural network architecture that
provides fast and accurate fluid flow predictions for 3D digital rock images. We trained our …

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

Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

We report the application of machine learning methods for predicting the effective diffusivity
(D e) of two-dimensional porous media from images of their structures. Pore structures are …

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

Boiling is arguably Nature's most effective thermal management mechanism that cools
submersed matter through bubble-induced advective transport. Central to the boiling …