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 be simulated, they can be inaccurate when capturing important physical phenomena such as puncture or tearing of the object. Here I will present the effects of various fidelity discretization on material failure quantities of interest and present machine learning techniques to map lower fidelity models to higher fidelity outputs.

Zoom invitation is sent to the seminar list. If you are not on the list, please, contact Arvind Saibaba to get the link