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      PPPL develops framework for unifying tokamak ML control models

      Princeton Plasma Physics Laboratory announced that researchers at the lab, in collaboration with Princeton University, have developed a general algorithm for prediction and control in tokamak systems and have tested it at DIII-D, as presented in a recent Nuclear Fusion paper.

      According to the paper, most machine learning (ML)–based tools for use in fusion machines have been implemented as stand-alone demonstrations, aiming to predict the plasma profile, suppress a form of instability, for example. PPPL’s project provides a framework that aims to accommodate these disparate models into an integrated system, which the team calls PACMAN (Prediction and Control Using Machine Learning).

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ANS STEM Academy Educator Series

Nuclear Energy: Fission, Fusion, and the Future

October 20, 2022|5:00–6:00PM (6:00–7:00PM EDT)

All UsersAvailable to All Users



Eric Loewen, chief consulting engineer for GE Hitachi Nuclear Energy and former American Nuclear Society president, presented this professional development opportunity created specifically for K- 12 science educators. Watch the recording to learn the basics and the latest about nuclear energy using fission and fusion to guide your students through the world of nuclear energy and technology. You’ll gain a deeper understanding of nuclear energy and classroom-resources you can use immediately.


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