Data-driven enhancement of fracture paths in random composites

A data-driven framework for the enhancement of fracture paths in random heterogeneous microstructures is presented. The approach relies on the combination of manifold learning, introduced to explore the geometrical structure exhibited by crack patterns and achieve efficient dimensionality reduction, and a posteriori crack path reconstruction, defined through a Markovianization. The proposed methodology enables the generation of new crack...