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

Abstract

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. In this paper, we focus on the two-phase flow in porous media problem. We provide two numerical examples to show the effectiveness of the new algorithm. It is found that adaptivity is essential to capture moving flow fronts. We show how the results obtained through this approach are more accurate than using RAR method or PINN with fixed collocation points, while having a comparable computational cost.

References in corpus (1)

• Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture

Cited by in corpus (4)

• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
• Deep Learning in Deterministic Computational Mechanics
• Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
• Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs