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U.S. PACMAN AI Framework Achieves Millisecond-Level Control of Fusion Plasma

The PACMAN AI framework, developed by researchers at the U.S. Department of Energy's Princeton Plasma Physics Laboratory (PPPL) and Princeton University, has completed five experimental validations in real fusion experimental systems. The framework can complete data processing, state prediction, and control command output in approximately 20 milliseconds, addressing rapidly developing instabilities in fusion plasma. This is an artist's rendition of the PACMAN AI framework for fusion systems. (Illustration credit: Kyle Palmer / PPPL Communications Department) PACMAN stands for Prediction and Control via Machine Learning. Related design and preliminary experimental results have been published in...

2026-09-03

Machine learning framework predicts micro-displacements of DIII-D fusion device coils

Researchers at the U.S. Department of Energy's (DOE) Thomas Jefferson National Accelerator Facility (Jefferson Lab) and collaborating teams have developed a machine learning framework that can predict subtle changes in critical fusion device hardware before the next experiment begins. The method targets the DIII-D National Fusion Facility in San Diego, and the findings have been published in the journal Machine Learning with Applications. Interior of the DIII-D National Fusion Facility tokamak. (Image courtesy of General Atomics) DIII-D is a tokam...

2026-08-17

Sophelio Launches Fusion Equilibrium Challenge, Opening DIII-D and MAST Experimental Datasets

On August 11, Sophelio launched the Fusion Equilibrium Challenge. The competition has been accepted into the 2026 Conference on Neural Information Processing Systems (NeurIPS) competition track, making it the first fusion energy-related challenge to enter this track. The challenge is co-organized by Sophelio together with the DIII-D National Fusion Facility, the UK Atomic Energy Authority (UKAEA) FAIR-MAST project, and the Institute for Fusion Studies (IFS) at the University of Texas at Austin, with data hosted on the Hugging Face platform. Open to the global machine learning community, the competition re...

2026-08-12