Determining anomalies in data using classical machine learning techniques typically requires characterizing the notion of what is "normal" or "expected" in the instance space. Upon doing so, one would then utilize this profile to identify points that do not coincide with this description of normal. However, this process tends to be costly computationally, thus limiting…

Often in large numerical simulations decisions are made to reduce the fidelity of particular features in order to simulate the event duration. One common method is the application of shell formulations instead of 3D continuum, especially for objects with large aspect ratios of extent to thickness. While these reductions allow for longer duration events to…