AI has taken on a big challenge — it can detect warning signs of plasma instabilities & predict them before they disrupt the extreme conditions needed for fusion energy

Fusion energy has a timing problem.

Inside a tokamak, plasma must remain extraordinarily hot and stable for researchers to sustain a fusion reaction. But small disturbances can grow in milliseconds, creating instabilities that can disrupt the plasma before a human operator has enough time to respond.

Researchers at Princeton University and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL) have now tested an artificial intelligence system designed to make those decisions at machine speed.


Called PACMAN, the framework can analyse plasma conditions and send control commands in milliseconds. In one experiment, it predicted a potentially damaging tearing-mode instability roughly 200 milliseconds before it appeared and adjusted the plasma to prevent it from developing.

The work could offer a new approach to one of the biggest challenges facing practical fusion energy: keeping a reaction stable long enough to make the technology useful.

Why fusion plasma is so difficult to control

Fusion attempts to recreate the process that powers the Sun.

On Earth, researchers use machines called tokamaks to confine an electrically charged gas, or plasma, with powerful magnetic fields. The plasma must be kept at extremely high temperatures and carefully controlled to maintain the conditions needed for fusion.

The problem is that plasma can be unpredictable.

An instability that begins as a small disturbance can grow extremely quickly. By the time a human operator notices the problem and reacts, the opportunity to prevent it may already have passed.

Conventional computer simulations do not necessarily solve the problem either. Some detailed plasma calculations can take far too long to provide useful information during an experiment that lasts only a few minutes.

That is where machine learning could make a difference.

The AI makes decisions in milliseconds

PACMAN stands for Prediction And Control using MAchiNe learning.

Rather than relying on a single AI model, the framework allows multiple machine learning models and control systems to work together.

The system continuously receives information from the tokamak, including measurements related to plasma temperature, density and magnetic conditions. It processes those signals, uses AI models to estimate what is happening or what may happen next, and then determines how the machine should respond.

The entire control framework typically operates in about 20 milliseconds and can repeat the process continuously.

That speed is critical.

A human operator may respond on a timescale of seconds, while some plasma instabilities can develop thousands of times faster.

The AI spotted a dangerous instability before it appeared

One of the most significant tests involved a phenomenon known as a tearing mode.

Tearing modes can disrupt the magnetic structure that confines plasma. Conventional controllers generally respond after the instability has already started.

PACMAN took a different approach.

In the experiment, a machine learning model predicted the tearing mode approximately 200 milliseconds before it occurred. The control system then changed the plasma conditions to prevent the instability from developing.

That distinction is important for fusion research.

Instead of waiting for a problem to appear and then trying to suppress it, an AI system could potentially recognise the warning signs and intervene before the instability becomes disruptive.

Researchers tested the system on a real tokamak

The Princeton team did not test PACMAN only in computer simulations.

The framework was deployed on the DIII-D National Fusion Facility tokamak in San Diego, where researchers carried out five separate experiments.

The tests covered several different plasma-control tasks.

PACMAN was used to operate heating systems with a reinforcement-learning model, predict bursts of energy from the plasma edge, control waves driven by fast particles and adjust plasma density and rotation.

It was also used to predict and prevent the tearing-mode instability.

The experiments demonstrate that the framework can bring several AI-based control functions together rather than treating each problem as an isolated demonstration.

Six heating systems were controlled at once

Another test highlighted how quickly the system can coordinate complicated machinery.

DIII-D uses six gyrotrons to heat plasma with powerful microwave beams. PACMAN was able to coordinate their operation simultaneously, adjusting both the power delivered by the gyrotrons and the position of their mirrors.

The goal was to reach targets established by researchers before the experiment began.

The system found a way to coordinate the different controls in real time, something the researchers say had not previously been handled by an algorithm in the same way.

This kind of coordination could become increasingly important as fusion experiments become more complex.

AI is not replacing the scientists

Despite the impressive speed, PACMAN does not operate as an independent decision-maker with unlimited control over the fusion machine.

The researchers built safety restrictions directly into the framework.

If an AI model recommends an action, the system still checks the command against hardware limits before sending it to the tokamak. Scientists also establish the objectives and review the results after experiments.

In other words, AI handles the extremely fast decisions, while humans remain responsible for deciding what the system is supposed to achieve.

That distinction could be especially important as artificial intelligence becomes more deeply integrated into experimental energy systems.

A faster way to develop fusion experiments

The researchers say PACMAN could also change how quickly new AI controllers are tested.

Building the initial framework and installing the first model took months. But once the infrastructure was established, adding another model reportedly took only a few days.

That could allow researchers to train, test and refine new models much more rapidly.

Instead of building a separate control system for every new fusion problem, scientists could add or replace individual AI components while leaving the rest of the framework intact.

The underlying research paper describes PACMAN as an integrated real-time control architecture designed to combine machine-learning predictors, controllers and other control components on DIII-D.

What this could mean for future fusion reactors

Fusion has long been viewed as a potential source of abundant low-carbon electricity, but maintaining a stable plasma remains one of the central technical challenges.

AI does not solve all of those problems.

The current experiments were carried out on a research tokamak, and the researchers still need to determine how well the approach performs across different machines, plasma conditions and control challenges.

The PACMAN framework also has limits. Its millisecond operating timescale is suitable for many plasma instabilities, but it would not be appropriate for phenomena that develop and disrupt on sub-millisecond timescales.

Still, the experiments demonstrate something significant: machine learning can move beyond analysing fusion data after an experiment and begin making real-time decisions while the plasma is actually being controlled.

The future of fusion may depend on predicting problems early

For decades, fusion researchers have worked to keep plasma hot, dense and stable.

Now, artificial intelligence is giving scientists another way to approach the problem: predict what the plasma is about to do and intervene before it becomes unstable.

The Princeton experiments do not mean commercial fusion power is suddenly ready.

But they show how AI could become part of the control architecture needed to operate increasingly sophisticated fusion machines.

And when a plasma can change in milliseconds, the ability to see a problem coming — even a fraction of a second in advance — could be far more valuable than simply reacting to it after it arrives.