Ejecta production in shocked systems is affected by surface geometry, impact parameters, and other material properties. These systems can be modeled well using simulations, but only with very high-fidelity simulations that require a large amount of computational time to complete. In this talk I will present work showing the utility of various machine learning techniques to ejecta physics, including unsupervised clustering of different types of ejecta particles, binary classification of which systems will and will not produce ejecta, and supervised learning of surface evolution after shock impact. The results from the use of each of these techniques provide motivation for further studies of ejecta physics with machine learning.
Topic: BNL Particle Physics Seminar Mar 15 Monday
Time: Mar 15, 2021 03:00 PM Eastern Time (US and Canada)
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Meeting ID: 998 4765 1969
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Brett Viren, Hanyu Wei