Pages that link to "Item:Q2157149"

The following pages link to Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149):

Displaying 7 items.

• Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800) (← links)
• Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models (Q2112502) (← links)
• Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow (Q2131089) (← links)
• A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media (Q2681146) (← links)
• Prediction of permeability of porous media using optimized convolutional neural networks (Q2683510) (← links)
• Graph network surrogate model for subsurface flow optimization (Q6560712) (← links)
• Gradient-boosted spatiotemporal neural network for simulating underground hydrogen storage in aquifers (Q6669090) (← links)