Deep learning in pore scale imaging and modeling
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 …
Learning into the workflow, from image processing to simulating physical processes. In …
Machine learning in geo-and environmental sciences: From small to large scale
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 …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning
Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean
electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is …
electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is …
Application of microfluidics in chemical enhanced oil recovery: A review
Abstract In Chemical Enhanced Oil Recovery (CEOR), various chemicals such as polymer,
surfactant, alkaline, and nanoparticles are injected solely or in combination to mobilize the …
surfactant, alkaline, and nanoparticles are injected solely or in combination to mobilize the …
[HTML][HTML] Deep learning accelerated prediction of the permeability of fibrous microstructures
Permeability of fibrous microstructures is a key material property for predicting the mold fill
times and resin flow path during composite manufacturing. In this work, we report an efficient …
times and resin flow path during composite manufacturing. In this work, we report an efficient …
Multiscale fusion of digital rock images based on deep generative adversarial networks
Computation of petrophysical properties on digital rock images is becoming important in
geoscience. However, it is usually complicated for natural heterogeneous porous media due …
geoscience. However, it is usually complicated for natural heterogeneous porous media due …
Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images
Segmentation of 3D micro-Computed Tomographic (μ CT) images of rock samples is
essential for further Digital Rock Physics (DRP) analysis, however, conventional methods …
essential for further Digital Rock Physics (DRP) analysis, however, conventional methods …
ML-LBM: predicting and accelerating steady state flow simulation in porous media with convolutional neural networks
Fluid mechanics simulation of steady state flow in complex geometries has many
applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale …
applications, from the micro-scale (cell membranes, filters, rocks) to macro-scale …
RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising
M Yu, M Guo, S Zhang, Y Zhan, M Zhao… - Computers in Biology …, 2023 - Elsevier
A common problem in the field of deep-learning-based low-level vision medical images is
that most of the research is based on single task learning (STL), which is dedicated to …
that most of the research is based on single task learning (STL), which is dedicated to …
Flow-based characterization of digital rock images using deep learning
X-ray imaging of porous media has revolutionized the interpretation of various microscale
phenomena in subsurface systems. The volumetric images acquired from this technology …
phenomena in subsurface systems. The volumetric images acquired from this technology …