Neural networks and data-driven surrogate models for simulation of steady-state fracture growth


This work is devoted to an assessment of the application of machine learning algorithms in the prediction of a fracture's aspect ratio caused by the hydraulic fracturing. By the aspect ratio in this work is assumed the ratio of the larger half-axis of the fracture to the smaller one. The study shows the prospects of applying data-driven surrogate model methods (deep neural networks learning from data simulated by means of traditional solvers) to particle dynamics modelling of hydraulic fracturing. The solution obtained allows to predict the aspect ratio value quickly, and thus, to evaluate the volum