Supercomputers provide the computing power for complex physics simulations, but these simulations require frequent manual adjustments to pre-vent run-time failures. Machine learning is a potential solution for automating this process. The LLNL clinic team is developing a machine learning model appropriate for supercomputers that can learn from the output of physics simulations as they run in real time.