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Cracking the Code: Predicting Properties of Material Fracture Networks using Machine Learning (Allon Percus, CGU)
Cracking the Code: Predicting Properties of Material Fracture Networks using Machine Learning (Allon Percus, CGU)
Understanding how fluid flows through heterogeneous materials, and how it can make these materials fail, are among the hardest challenges in materials science. Experiments and simulations show that flow through subsurface rock is mostly limited to a small subnetwork, or backbone, of fractures. Identifying this backbone would allow for a large speedup in flow and […]