Accelerating continuum-scale brittle fracture simulations with machine learning

Simulating the behavior of macroscale brittle materials is extremely computationally expensive if every microcrack is explicitly simulated since a very fine grid is required to resolve these cracks. Continuum scale models that lump these microcracks into elastic moduli are much less computationally expensive but are prone to neglecting the physics of the subgrid-scale microcracks. We develop an approach that leverages data from an expensive simulator (HOSS) that explicitly resolves the behavior of microcracks to build a machine learning model of the impact of the microcracks on the elastic moduli. We then combine this machine learning model with a continuum scale model (FLAG) to produce a model (which we call ML-FLAG) that provides accuracy comparable to HOSS but is four orders of magnitude faster than HOSS. We validate our ML-FLAG model by comparing it to a flyer plate experiment.