Pages that link to "Item:Q2681146"

The following pages link to A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media (Q2681146):

Displaying 7 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)
• Prediction of permeability of porous media using optimized convolutional neural networks (Q2683510) (← links)
• Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains (Q6092912) (← links)
• Interface PINNs (I-PINNs): a physics-informed neural networks framework for interface problems (Q6588288) (← links)
• Improved physics-informed neural networks for the reinterpreted discrete fracture model (Q6648401) (← links)
• Gradient-boosted spatiotemporal neural network for simulating underground hydrogen storage in aquifers (Q6669090) (← links)