  Damage models for ductile materials typically need to be parameterized, often
with the appropriate parameters changing for a given material depending on the
loading conditions. This can make parameterizing these models computationally
expensive, since an inverse problem must be solved for each loading condition.
Using standard inverse modeling techniques typically requires hundreds or
thousands of high-fidelity computer simulations to estimate the optimal
parameters. Additionally, the time of a human expert is required to set up the
inverse model. Machine learning has recently emerged as an alternative approach
to inverse modeling in these settings, where the machine learning model is
trained in an offline manner and new parameters can be quickly generated on the
fly, after training is complete. This work utilizes such a workflow to enable
the rapid parameterization of a ductile damage model called TEPLA with a
machine learning inverse model. The machine learning model can efficiently
estimate the model parameters much faster, as compared to previously employed
methods, such as Bayesian calibration. The results demonstrate good accuracy on
a synthetic test dataset and is validated against experimental data.
