Pages that link to "Item:Q2683510"
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The following pages link to Prediction of permeability of porous media using optimized convolutional neural networks (Q2683510):
Displaying 12 items.
- A neural network approach for prediction of critical submergence of an intake in still water and open channel flow for permeable and impermeable bottom (Q416599) (← links)
- Porous structure reconstruction using convolutional neural networks (Q1622858) (← links)
- Prediction model of permeability index for blast furnace based on the improved multi-layer extreme learning machine and wavelet transform (Q1661448) (← links)
- The use of ANN for the prediction of the modified relative permeability functions in stratified reservoirs (Q1682805) (← links)
- Upscaling of two-phase discrete fracture simulations using a convolutional neural network (Q2085098) (← links)
- Automated porosity estimation using CT-scans of extracted core data (Q2147571) (← links)
- A data-driven surrogate to image-based flow simulations in porous media (Q2176870) (← links)
- Surrogate permeability modelling of low-permeable rocks using convolutional neural networks (Q2184490) (← links)
- Rapid estimation of permeability from digital rock using 3D convolutional neural network (Q2192827) (← links)
- A deep learning perspective on predicting permeability in porous media from network modeling to direct simulation (Q2192831) (← links)
- Physics-informed data-driven model for fluid flow in porous media (Q6093463) (← links)
- Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS (Q6106108) (← links)
