Artificial intelligence is providing scientists with a new and unprecedented view inside the chaotic environment of nuclear fusion reactors, overcoming the physical limits of conventional sensors. Researchers have developed an AI that can intelligently reconstruct missing data from the superheated plasma that fuels fusion reactions, a breakthrough that enhances plasma stability and accelerates the quest to develop clean, virtually limitless energy. This new technique allows for a far more detailed understanding of plasma behavior, which is critical to sustaining the fusion process and preventing damage to the reactors that house it.
This advance is one of several recent developments where AI and machine learning are being applied to solve long-standing challenges in fusion energy. By training algorithms on vast datasets from past experiments, research teams are now able to predict and prevent dangerous plasma instabilities, protect internal reactor components from extreme heat, and simulate the complex physics of fusion with greater speed and accuracy. These AI tools are becoming indispensable for designing the next generation of fusion devices and developing the control systems needed to operate them efficiently and safely, pushing the dream of a fusion-powered future closer to reality.
The Challenge of Incomplete Data
The inside of a tokamak, a donut-shaped device designed for magnetic confinement fusion, is one of the most extreme environments ever created on Earth. Within it, hydrogen isotopes are heated to temperatures hotter than the core of the sun, forming a state of matter called plasma. Containing and controlling this incredibly hot, turbulent plasma is the primary challenge of fusion energy. Scientists rely on a wide array of diagnostic sensors to measure properties like temperature, density, and magnetic fields, but these instruments have limitations. The intense heat and radiation can degrade or destroy sensors, while the complex geometry of the reactor means there are always blind spots where direct measurement is impossible.
These data gaps have significant consequences. Without a complete picture of the plasma’s state, it is difficult to maintain its stability. Sudden instabilities, known as disruptions or edge-localized modes (ELMs), can arise in milliseconds. These events can release massive bursts of energy that can damage the interior walls of the reactor, forcing costly shutdowns and repairs. To make fusion power plants commercially viable, operators need to be able to anticipate and mitigate these instabilities in real time, a task that requires more comprehensive data than sensors alone can provide.
A New Form of AI-Powered Perception
To solve the problem of missing data, researchers at Princeton University and the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), along with several partner universities, developed a novel AI system called Diag2Diag. This tool works by taking the data streams from existing, functioning sensors and using them to generate synthetic data for other diagnostics that may be failing, degraded, or entirely absent. The AI essentially learns the complex physical relationships between different plasma properties and can recreate what a missing sensor would have seen with remarkable accuracy. The system was trained and tested on experimental data from the DIII-D National Fusion Facility.
In a recent study, the team demonstrated that Diag2Diag can generate a synthetic version of a diagnostic measurement that is often richer in detail than what the physical sensors can capture. Lead author Azarakhsh Jalalvand of Princeton University explained that this allows scientists to “see” what their instruments miss. By providing a more complete and detailed view of the plasma’s state, the AI gives reactor operators a powerful tool for maintaining control and stability. The applications could extend beyond fusion to other complex systems like spacecraft or robotic surgery, where reliable sensor data is critical.
Validating Theories and Taming Instabilities
One of the most significant early applications of the Diag2Diag system has been in understanding how to control ELMs, the destructive energy bursts that pose a major threat to reactor components. One of the leading methods for suppressing ELMs is the application of small magnetic fields, known as resonant magnetic perturbations (RMPs), to the plasma’s edge. Scientists have theorized that RMPs work by creating tiny magnetic islands in the plasma that help to relieve pressure and prevent the buildup of energy that leads to an ELM. However, directly observing this phenomenon has been difficult.
The detailed, reconstructed data from Diag2Diag provided clear evidence supporting this theory. The AI-generated diagnostics offered much more detail on how the magnetic islands form and evolve in response to the RMPs, helping to confirm their role in stabilizing the plasma. This deeper understanding is crucial for refining the use of RMPs and other control techniques, ultimately making future fusion reactors more robust and reliable.
Broader AI Integration in Fusion Research
Protecting Reactor Components
Another critical challenge in fusion reactor design is protecting internal components from the intense heat of the plasma. Even with powerful magnetic fields for confinement, some heat inevitably escapes and strikes the interior walls. Identifying the precise locations where the heat load is highest is vital for designing durable components. To address this, researchers from Commonwealth Fusion Systems (CFS), PPPL, and Oak Ridge National Laboratory developed an AI tool called HEAT-ML. This model is trained to predict the locations of “magnetic shadows,” areas on the reactor wall that are shielded from direct plasma contact by other components.
Traditionally, calculating the path of magnetic field lines to identify these shadows was a computationally intensive process that could take 30 minutes for a single scenario. HEAT-ML, a deep learning model, reduces this time to mere milliseconds by recognizing patterns from thousands of previous simulations. This dramatic speed-up allows engineers to rapidly iterate on designs and integrate protective measures much more efficiently. It is currently being used to help design the SPARC fusion device, a compact, high-field tokamak being built in collaboration with MIT.
Real-Time Control and Prediction
Beyond design and analysis, AI is also being integrated directly into the control systems of operating fusion devices. At the Swiss Plasma Center, researchers collaborating with DeepMind developed a deep reinforcement learning AI to manipulate the plasma in the TCV tokamak in real time. The AI learned to control the powerful magnetic coils that shape and position the plasma, successfully sculpting it into various configurations. This demonstrated the potential for AI to manage the complex, dynamic behavior of plasma more effectively than conventional control systems.
AI is also being used to forecast deadly disruptions before they occur. At the DIII-D tokamak in the United States, a neural network was trained to recognize the subtle warning signs that precede a disruption. The model was able to successfully anticipate an impending disruption 300 milliseconds ahead of time, providing a crucial window for intervention to prevent the event and protect the machine. These predictive capabilities are essential for ensuring the safety and operational efficiency of future power plants.
The Future of Autonomous Fusion Reactors
The various AI tools being developed today represent crucial steps toward the ultimate goal of autonomous fusion reactor operation. For a fusion power plant to be a practical and economical source of energy, its complex systems will need to be managed with minimal human intervention. AI models that can predict instabilities, optimize plasma conditions, and protect machine components will form the backbone of these future control systems.
Researchers are now working to generalize these specialized AI models so they can be applied to different reactor designs and a wider range of plasma conditions. For example, the team behind HEAT-ML hopes to create a version that works across various tokamak shapes and sizes, not just the specific design of SPARC. Similarly, simulation tools enhanced with machine learning, such as the PORTALS code developed at MIT, are being used to create “surrogate” models that can rapidly predict how plasma will behave in future devices like ITER. This “predict-first” approach, grounded in data from past experiments, allows scientists to design more efficient and effective fusion reactors, accelerating the timeline toward delivering clean fusion energy to the grid.