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

A.V. Kalyuzhnyuk, R.L. Lapin, A.S. Murachev, A.E. Osokina, A.I. Sevostianov & D.V. Tsvetkov
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...
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