Pages that link to "Item:Q2671397"
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The following pages link to A gradient-based deep neural network model for simulating multiphase flow in porous media (Q2671397):
Displaying 10 items.
- Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow (Q2131089) (← links)
- Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
- A deep learning based reduced order modeling for stochastic underground flow problems (Q2162031) (← links)
- Prediction of permeability of porous media using optimized convolutional neural networks (Q2683510) (← links)
- (Q4625371) (← links)
- AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems (Q5099843) (← links)
- Using Karhunen-Loève decomposition and artificial neural network to model miscible fluid displacement in porous media (Q5950152) (← links)
- A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion (Q6044223) (← links)
- Prediction of numerical homogenization using deep learning for the Richards equation (Q6098948) (← links)
- Gradient-boosted spatiotemporal neural network for simulating underground hydrogen storage in aquifers (Q6669090) (← links)
