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References from Prediction of effective diffusivity of porous media using deep learning method based on sample structure information self-amplification. Local targets link to admitted publications; unresolved targets remain external evidence.
Pore-scale modeling of effective diffusion coefficient of building materials
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Effective transport properties for polymer electrolyte membrane fuel cells – With a focus on the gas diffusion layer
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Coupled GCMC and LBM simulation method for visualizations of CO2/CH4 gas separation through Cu-BTC membranes
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Pore-scale investigation on coupled diffusion mechanisms of free and adsorbed gases in nanoporous organic matter
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Modeling of multi-scale transport phenomena in shale gas production — A critical review
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Unresolved reference
2007 · External reference
Mass transfer in mesoporous materials: the benefit of microscopic diffusion measurement
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Subcontinuum mass transport of condensed hydrocarbons in nanoporous media
10.1038/ncomms7949 · 2015 · External reference
Mesoscale modeling in electrochemical devices—a critical perspective
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Deconstructing electrode pore network to learn transport distortion
10.1063/1.5124099 · 2019 · External reference
Mass transfer in mesoporous materials: the benefit of microscopic diffusion measurement
10.1039/c3cs35326e · 2013 · External reference
Calculation of the effective diffusivity of heterogeneous media using the lattice-Boltzmann method
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Review of molecular simulation method for gas adsorption/desorption and diffusion in shale matrix
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Prediction of the effective thermal conductivity of an adsorption bed packed with 5A zeolite particles under working conditions
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Effect of pore structure, randomness and size on effective mass diffusivity
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Gas diffusion in porous media
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Investigation on mass diffusion process in porous media based on Lattice Boltzmann method
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Effective gas diffusion coefficient in fibrous materials by mesoscopic modeling
10.1016/j.ijheatmasstransfer.2016.11.097 · 2017 · External reference
Three-dimensional pore-scale study of methane gas mass diffusion in shale with spatially heterogeneous and anisotropic features
10.1016/j.fuel.2020.117750 · 2020 · External reference
Efficient deep learning techniques for multiphase flow simulation in heterogeneous porousc media
10.1016/j.jcp.2019.108968 · 2020 · External reference
An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning
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10.1016/j.egyai.2020.100012 · 2020 · Admitted local publication
A machine learning approach to filtrate loss determination and test automation for drilling and completion fluids
10.1016/j.petrol.2019.106727 · 2020 · External reference
A data-driven surrogate to image-based flow simulations in porous media
10.1016/j.compfluid.2020.104475 · 2020 · External reference
Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods
2020 · External reference
PoreFlow-net: a 3D convolutional neural network to predict fluid flow through porous media
10.1016/j.advwatres.2020.103539 · 2020 · External reference
Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods
10.1016/j.ijheatmasstransfer.2018.08.082 · 2018 · External reference
Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods
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Predicting effective diffusivity of porous media from images by deep learning
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Modeling nanoconfinement effects using active learning
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Deep learning for a fast and accurate prediction of complex carbonate rock permeability from 3D micro-CT images
2019 · External reference
Machine learning-based design of porous graphene with low thermal conductivity
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Direct simulation of supercritical gas flow in complex nanoporous media and prediction of apparent permeability
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Mesoscopic predictions of the effective thermal conductivity for microscale random porous media
10.1103/physreve.75.036702 · 2007 · External reference
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10.1063/1.869307 · 1997 · External reference
Microstructure effects on effective gas diffusion coefficient of nanoporous materials
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2011 · External reference
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Automatic differentiation in PyTorch
2017 · External reference
Effective diffusion coefficient in porous media
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Pore-scale prediction of transport properties in reconstructed nanostructures of organic matter in shales
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