We present an approach based on machine learning (ML) to distinguish eruption and
precursory signals of Chimayó geyser (New Mexico, USA) under noisy environmental …

Traditional stochastic algorithms for characterizing fracture networks are purely based on
statistical inferences from outcrop images, and therefore the models produced, may not be …

Natural fractures control the flow of subsurface fluids; however, uncertainties associated with
their prediction are likely. Therefore, the stochastic characterization of these fractured …

Failure in brittle materials under dynamic loading conditions is a result of the propagation
and coalescence of microcracks. Simulating this discrete crack evolution at the continuum …

In brittle fracture applications, failure paths, regions where the failure occurs and damage
statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle …

Reservoir simulation is the industry standard for prediction and characterization of
processes in the subsurface. However, simulation is computationally expensive and time …

The presence, evolution, and coalescence of cracks affect the strength and damage of
materials. However, simulating cracks in a finite or discrete element framework (high-fidelity) …