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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).
Andries van Hattem (Delft Univ. Technology), Karin Popa (European Commission, Joint Research Centre), Jean-Christophe Griveau (European Commission, Joint Research Centre), Eric Colineau (European Commission, Joint Research Centre), Anna L. Smith (Delft Univ. Technology), Rudy J.M. Konings (Delft Univ. Technology)
Proceedings | Plutonium Futures - The Science 2024 | Charleston, SC, September 8-12, 2024 | Pages 98-98
