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The TIGER HPC computing cluster in the High Performance Computing Research Center, Princeton’s 47,000-square-foot data center.
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Four members of the PICSciE-affiliated research team that designed the experiments and developed the FRRN code: Chief architect of the FRNN is Julian Kates-Harbeck (inset photo), a graduate student at Harvard University and a DOE Office of Science Computational Science Graduate Fellow; Eliot Feibush (standing, center), a computational scientist and experienced data analyst at PPPL, Dr. Alexey Svyatkovskiy (seated), a deep learning/AI expert and collaborator, and Dr. William Tang, lead researcher. Photo credit: Elle Starkman, Princeton Plasma Physics Laboratory
In scientific research, results don’t just have to be good—they have to be reproducible. Members of the PICSciE-affiliated research team are achieving both, as they apply a deep-learning AI approach to the problem of disruption forecasting in tokamak fusion plasmas.-
Princeton’s Research Software Engineering group is in high demand, reflecting a growing trend in academic research.
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Inventor Jeroen Tromp, the Blair Professor of Geology and professor of geosciences and applied and computational mathematics
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The amount of data encompassed by the global climate in its entirety, from its winds and temperature conditions to shifting formations of cloud, is almost unimaginably complex. Layer in the ever-evolving small- and large-scale interactions involved, and the thought of creating a model of the entire Earth system seems beyond possibility.
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Turbulent combustion is just that. Jet engines and high-power furnaces produce a great amount of energy, along with its byproduct: emissions in the form of soot and other polluting materials.
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The most mysterious objects in the universe can shed light on important phenomena here on Earth. With this in mind, one Princeton professor has set out to quantify the behavior of black holes at a level of detail never before achieved.
- Enabling high-impact research by bringing education and advanced computing to the Princeton community
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Workshops
Computational and Data Science for High Energy Physics (CoDaS-HEP)
Mon, Jul 23, 2018, 8:30 am to Fri, Jul 27, 2018, 5:30 pm
Location:
PCTS, 407 Jadwin Hall
Help Sessions
Weekly Help Sessions
Weekly Help Sessions in Visualization & Programming
Thu, Jul 26, 2018, 2:00 pm to 3:00 pm
Location:
347 Lewis Science Library
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