  Simulation of the crack network evolution on high strain rate impact
experiments performed in brittle materials is very compute-intensive. The cost
increases even more if multiple simulations are needed to account for the
randomness in crack length, location, and orientation, which is inherently
found in real-world materials. Constructing a machine learning emulator can
make the process faster by orders of magnitude. There has been little work,
however, on assessing the error associated with their predictions. Estimating
these errors is imperative for meaningful overall uncertainty quantification.
In this work, we extend the heteroscedastic uncertainty estimates to bound a
multiple output machine learning emulator. We find that the response prediction
is accurate within its predicted errors, but with a somewhat conservative
estimate of uncertainty.
