Randy Churchill
*The final, formatted version of the article will be published soon.
PERSPECTIVE article
Front. Phys.
Sec. Fusion Plasma Physics
Volume 12 - 2024 |
doi: 10.3389/fphy.2024.1531334
This article is part of the Research Topic Visualizing Offline and Live Data with AI (VOLDA) Workshop first edition Princeton 11-13th June 2024 View all articles
AI foundation models for experimental fusion tasks
Provisionally accepted- Princeton Plasma Physics Laboratory (DOE), Plainsboro Center, United States
AI foundation models while successful in various domains of language, speech, vision, and beyond, have not been adopted in production for fusion energy experiments. This brief paper presents how AI foundation models can be used for fusion energy diagnostics, enabling for example visual automated logbooks to provide greater insight to chains of plasma events in a discharge, in time for between shot analysis.
Keywords: fusion energy, Aritificial Intelligence, machine learning, Foundation models, diagnostic
Received: 20 Nov 2024; Accepted: 12 Dec 2024.
Copyright: © 2024 Churchill. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Randy Churchill, Princeton Plasma Physics Laboratory (DOE), Plainsboro Center, United States
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