A heteroencoder architecture for prediction of failure locations in porous metals using variational inference. (arXiv:2202.00078v1 [physics.app-ph])

In this work we employ an encoder-decoder convolutional neural network to
predict the failure locations of porous metal tension specimens based only on
their initial porosities. The process we model is complex, with a progression
from initial void nucleation, to saturation, and ultimately failure. The
objective of predicting failure locations presents an extreme case of class
imbalance since most of the material in the specimens do not fail. In response
to this challenge, we develop and demonstrate the effectiveness of …

app architecture arxiv failure metals physics prediction