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The use of AI and ML for scientific applications is currently a very excitingand dynamic field. Much of this excitement for HPC has focused on MLapplications whose analysis and classification generate very large numbers offlops. Others seek to replace scientific simulations with data-driven surrogatemodels. But another important use case lies in the combination application ofML to improve simulation accuracy. To that end, we present an anomaly problemwhich arises from a core-collapse supernovae simulation. We discuss strategiesand early successes in applying anomaly detection techniques from machinelearning to this scientific simulation, as well as current challenges andfuture possibilities.

Towards the Development of Entropy-Based Anomaly Detection in an Astrophysics Simulation