Predicting flow in porous media: a comparison of physics-driven neural network approaches

This paper presents the development of physics-informed machine learning models for subsurface flows, specifically for determining pressure variation in the subsurface without the use of numerical modeling schemes. The numerical elliptic operator is replaced with a neural network operator and includes comparisons of several different machine learning models, along with linear regression, support vector regression, lasso, random forest regression, decision tree regression, light weight gradient boosting, eXtreme gradient boosting, convolution neural network, artificial neural network, and perceptron. The mean of absolute error of all models is compared, and error residual plots are used as a measure of error to determine the best-performing method.