AI revolutionizes fusion energy, bringing us closer to a more sustainable future

A team of Princeton researchers has brought what was once considered a pipe dream closer to reality: the realization of fusion energy. This breakthrough, backed by artificial intelligence, marks a potential turning point in our approach to energy production. The work was published in the journal Nature.

Fusion energy is the process in which atomic nuclei combine to form new nuclei, releasing massive amounts of energy. It has long been touted as a clean, safe, and virtually inexhaustible source of energy. In addition, it offers almost four million times more energy per mass than traditional fossil fuels.

Unfortunately, achieving fusion on Earth has proven to be a formidable challenge due to the extreme temperatures and pressures needed to sustain the reaction, conditions typically only found in stars. Therefore, researchers have turned to plasma, a superheated state of matter. However, when inside a reactor, the plasma loses stability and easily escapes containment, thus stopping the fusion process.

Overcoming the challenge

The new study details the development of an innovative approach to address plasma instabilities in the pursuit of fusion energy. Using data from previous experiments at the DIII-D National Fusion Facility in San Diego, the team has trained an artificial intelligence (AI) to predict and prevent these instabilities before they occur, marking a change from previous methods.

The AI is not based on physical models, but rather learns from historical data about plasma behavior. This allows you to create a control strategy that keeps the plasma stable and high power in real time.

"The AI was not taught the complex physics of fusion," explained Azarakhsh Jalalvand, co-author of the study. "It was given clear objectives: maintain a high-power reaction and avoid instabilities, learning over time the best path to these objectives."


Practical implementation of this AI in D-III D facilities has shown promising results. The model successfully predicts and avoids tear instabilities, a common form of plasma instability, up to 300 milliseconds in advance. Although this time interval may seem short, it is enough for the AI to adjust the reactor parameters and stabilize the plasma.

Despite these achievements, the path to practical-scale fusion energy still presents challenges. Nevertheless, the Princeton team's work marks significant progress and an important step toward a cleaner, more sustainable future, bringing us closer to realizing nuclear fusion, previously considered unattainable.

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