148 citations · 183 across the 5 of their papers we have counts for
6 papers
Characterization of wetting using topological principles
Chenhao Sun, James E. McClure, Peyman Mostaghimi +5
Hypothesis Understanding wetting behavior is of great importance for natural systems and technological applications. The traditional concept of contact angle, a purely geometrical…
ML-LBM: Machine Learning Aided Flow Simulation in Porous Media
Ying Da Wang, Traiwit Chung, Ryan T. Armstrong +1
Simulation of fluid flow in porous media has many applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale (groundwater, hydrocarbon reservoirs, and geoth…
Physical Accuracy of Deep Neural Networks for 2D and 3D Multi-Mineral Segmentation of Rock micro-CT Images
Ying Da Wang, Mehdi Shabaninejad, Ryan T. Armstrong +1
Segmentation of 3D micro-Computed Tomographic uCT) images of rock samples is essential for further Digital Rock Physics (DRP) analysis, however, conventional methods such as thresh…
Linking continuum-scale state of wetting to pore-scale contact angles in porous media
Chenhao Sun, James E. McClure, Peyman Mostaghimi +4
Wetting phenomena play a key role in flows through porous media. Relative permeability and capillary pressure-saturation functions show a high sensitivity to wettability, which has…
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning
Ying Da Wang, Ryan T. Armstrong, Peyman Mostaghimi
Digital Rock Imaging is constrained by detector hardware, and a trade-off between the image field of view (FOV) and the image resolution must be made. This can be compensated for w…
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images
Ying Da Wang, Ryan Armstrong, Peyman Mostaghimi
Single Image Super Resolution (SISR) techniques based on Super Resolution Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography (μCT) images of sandstone a…