Data Scientist Spotlight
Preston Piercey
DSSI Summer Student
Where do you attend school, and what are you studying?
I attend Texas A&M, and I’ll be starting a master’s in computer science there this fall.
Where are you from?
I am from Oklahoma City, Oklahoma, and moved to Texas for undergrad.
Recent Research
Tri-Lab Collaboration Produces Featured Article on Bayesian Analysis for Shock Compression
A collaboration between scientists at Lawrence Livermore, Los Alamos, and Sandia national laboratories recently led to a paper published in the Journal of Applied Physics. The paper, titled “A Tutorial on Bayesian Analysis of Linear Shock Compression Data,” was selected by the journal editors as a Featured Article, a designation given to papers considered to be among the journal's best.
The paper demonstrates how to apply Bayesian analysis to shock wave-particle velocity data from shock-compression experiments and how to propagate the resulting uncertainty to important material properties like density and pressure. The Bayesian approach generates multiple Hugoniot curves that are consistent with the experimental data, in contrast to the more traditional least-squares approach that generates a single curve. These Hugoniot curves provide information needed to develop equation-of-state (EOS) models, which are crucial for simulations of complex phenomena, including inertial confinement fusion, materials under shock compression, and planetary dynamics.
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