Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet

Understand

We predict steady-state Stokes flow of fluids within porous media at pore scales using sparse point observations and a novel class of physics-informed neural networks, called "physics-informed PointNet" (PIPN).

• Taking the advantages of PIPN into account, three new features become available compared to physics-informed convolutional neural networks for porous medium applications.
• First, the input of PIPN is exclusively the pore spaces of porous media (rather than both the pore and grain spaces).
• This feature diminishes required computer memory.