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PPPL's PACMAN framework lets multiple AI models control a tokamak in ~20 ms, preventing a tearing mode

★★after cutoffsciencePrinceton Plasma Physics LaboratoryGeneral Atomicsconfidence: medium

PPPL reported PACMAN, a modular framework that plugs several ML models directly into a tokamak's control system, reading plasma data and issuing commands in about 20 ms. In five DIII-D experiments an RL model took full control of the heating systems, and the framework predicted edge bursts (ELMs), controlled fast-particle-driven waves, and predicted and prevented a tearing mode.

Key facts

Science result

Field
physics / nuclear fusion / plasma control
Problem
Integrating multiple AI predictors and controllers safely into real-time fusion operation
Result
Modular real-time AI control framework demonstrated on DIII-D across several control tasks.
AI system
PACMAN framework (RL and predictive models)
Human role
Human-designed; humans set goals and safety limits
Verification
Peer-reviewed in Nuclear Fusion; hardware demonstrations
Status
confirmed

What happened

PPPL moved from single-purpose AI controllers to a framework where several models share control of one machine in real time.

Why it matters

It is a step toward the AI-supervised operation that future power-plant tokamaks such as SPARC and ITER are expected to need.

Changelog

  • 2026-09-29: created

Related events

  1. AI controller predicts and avoids tearing instabilities in the DIII-D fusion reactor ★★★
  2. Deep reinforcement learning controls fusion plasma in the TCV tokamak ★★★★
  3. Google DeepMind partners with Commonwealth Fusion Systems to optimise and control the SPARC tokamak with AI ★★★

Sources (3)

id: 2026-09-03-pppl-pacman-fusion-ai-control · updated 2026-09-29 · open in the interactive timeline