8 citations · 8 across the 1 of their papers we have counts for
3 papers
cs.LG2020★ 8 cited
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Yinan Wang, Diane Oyen, Weihong +7
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal…
cond-mat.mtrl-sci2020
Accelerating High-Strain Continuum-Scale Brittle Fracture Simulations with Machine Learning
M. Giselle Fernández-Godino, Nishant Panda, Daniel O'Malley +4
Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this mechanism at the continuum level is com…
cs.CE2018
Estimating Failure in Brittle Materials using Graph Theory
M. K. Mudunuru, N. Panda, S. Karra +5
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…