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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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Advances in Digital Twins for Nuclear Safeguards

August 28, 2026|11:00AM–12:00PM (12:00–1:00PM EDT)

All UsersAvailable to All Users



Co-hosted by ANS and the Institute of Nuclear Materials Management

As digital technologies continue to evolve, digital twins are opening new possibilities for the future of nuclear safeguards. Combining advanced simulation, integrated sensor data, and real-time system modeling, these tools can improve inspection planning, support remote verification, and enhance the ability to identify anomalies before they become significant issues.

Join experts in digital twin research as they discuss recent innovations, evolving capabilities, and promising applications for nuclear safeguards. Panelists will also share their perspectives on where digital twins are headed and the role they may play in strengthening safeguards in the years ahead.

Panelists

  • Manuel Kreutle, Forschungszentrum Julich
  • Nick Luciano, University of Texas at Austin
  • Ryan Stewart, Idaho National Laboratory
  • Pavel Tsvetkov, Texas A&M University

Moderator

  • Luis Ocampo Giraldo, National Nuclear Security Administration
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