Neural solution of elliptic partial differential equation problem for single phase flow in porous media

Partial differential equations are used to model fluid flow in porous media. Neural networks can act as equation solution approximators by basing their forecasts on training samples of permeability maps and corresponding two-point flux approximation solutions. This paper illustrates how convolutional neural various architecture, depth parameter configurations manage forecast solutions the Darcy’s for domain sizes.