Residual-based adaptivity for two-phase flow simulation in porous media using Physics-informed Neural Networks

Understand

This paper aims to provide a machine learning framework to simulate two-phase flow in porous media.

• The proposed algorithm is based on Physics-informed neural networks (PINN).
• A novel residual-based adaptive PINN is developed and compared with the residual-based adaptive refinement (RAR) method and with PINN with fixed collocation points.
• The proposed algorithm is expected to have great potential to be applied to different fields where adaptivity is needed.