(656c) Physics-Informed Deep Neural Networks for Predicting Fluid Flow in Complex Porous Materials
AIChE Annual Meeting
2022
2022 Annual Meeting
Computational Molecular Science and Engineering Forum
Machine Learning for Soft Materials II
Thursday, November 17, 2022 - 4:05pm to 4:20pm
Fluid flow in heterogeneous porous media arises in many systems, from biological tissues to composite materials, soil, wood, and paper. With advances in instrumentations, high-resolution images of porous media can be obtained and used directly in the simulation of fluid flow. The computations are, however, highly intensive. Although machine learning (ML) algorithms have been used for predicting flow properties of porous media, they lack a rigorous, physics-based foundation and rely on correlations. We introduce an ML approach that incorporates mass conservation and the NavierâStokes equations in its learning process. By training the algorithm to relatively limited data obtained from the solutions of the equations over a time interval, we show that the approach provides highly accurate predictions for the flow properties of porous media at all other times and spatial locations, while reducing the computation time.