DATE:

Friday, July 16, 2021

TIME:

3:30 PM

LOCATION:

Virtual Zoom Conference

SPEAKER:

Jordan Fox, PhD Candidate, Computational Science, San Diego State University and Kyle A. Wendt, Lawrence Livermore National Laboratory

ABSTRACT:

Machine learning methods are used to analyze systematic trends in nuclear reaction cross section evaluations over the nuclear landscape. We employ a system of multiple generative adversarial neural networks to learn how a cross section changes when proton- and/or neutron-number change. The model uses an ensemble of neural networks to predict cross sections, which helps to identify anomalies in libraries and to make extrapolations to where experiments cannot be done. This work is the foundation for a larger system that can incorporate correlations between reaction channels and enhance our understanding of trends in reaction data. Supported in part by LLNL under DOE Contract DE-AC52-07NA27344 and DOE Grant DE-FG02-03ER4127.

HOST:

Abraham Flores and the SDSU SIAM Student Chapter

VIDEO: