AI Brings a New Eye to Fusion Energy Diagnostics
A new artificial intelligence system, Diag2Diag, aims to fill gaps in fusion diagnostics by turning available sensor data into detailed, synthetic information that other sensors would reveal. Led by Azarakhsh Jalalvand at Princeton University and published in Nature Communications, the work demonstrates how AI can align synthetic data with real measurements, often offering more detail than a single diagnostic could provide. The approach could make fusion systems more robust, reduce hardware complexity, and lower costs—paving the way for reliable, 24/7 fusion energy in the future.
How Diag2Diag Works
Diagnostics in a fusion device measure many plasma characteristics, from temperature to density and beyond. Some measurements, such as those at the plasma edge (the pedestal), are notoriously difficult to capture at high speed. Diag2Diag tackles this by learning relationships between different diagnostics: data from one set of sensors can be used to infer what another diagnostic would observe. In effect, the AI fills in missing data gaps, producing a coherent, high-resolution view of the plasma state that aligns with reality.
The work also highlights how the synthetic data can reveal fast-evolving plasma instabilities that are hard to catch with traditional diagnostics. For instance, Thomson scattering—an established diagnostic in tokamaks—provides electron temperature and density measurements but often lacks the temporal or spatial resolution at the pedestal. By enhancing this diagnostic with AI-generated detail, researchers gain a clearer picture of the edge conditions that govern stability and performance.
Why This Matters for Fusion Energy
For fusion to become a practical power source, devices must operate reliably around the clock. Current experiments are highly instrumented, but adding more hardware can introduce complexity, maintenance needs, and costs. Diag2Diag offers a way to strengthen monitoring and control without proliferating sensors. In the words of the team, this approach can effectively “boost” existing diagnostics while keeping hardware budgets in check—an essential step toward compact, economical fusion systems.
Moreover, the AI-supported data supports theoretical models of how edge conditions influence overall stability. In particular, researchers are investigating edge-localized modes (ELMs)—bursts that can damage reactor walls. One strategy to mitigate ELMs uses resonant magnetic perturbations (RMPs) to reshape the plasma edge. The enriched data from Diag2Diag provides new evidence about how magnetic islands and the flattening of temperature and density profiles in the pedestal evolve, informing better control strategies and design choices for future reactors.
Broader Implications and Future Applications
Diag2Diag is not limited to fusion. The concept—reconstructing missing diagnostic data to improve reliability—could apply to spacecraft systems, robotic surgery, and any field where sensor degradation or gaps threaten safety and performance. The international collaboration behind the work, spanning Princeton, PPPL, Chung-Ang University, Columbia University, and Seoul National University, underscores the broad interest in robust, data-driven diagnostics across disciplines.
What Lies Ahead
The researchers are exploring expanding Diag2Diag to additional diagnostics and other fusion facilities. As plans progress, the AI could help future commercial reactors be more compact, with fewer nonessential components and lower maintenance costs. The overarching goal is a fusion energy landscape where reliable, continuous operation is built into the diagnostic backbone—reducing downtime and accelerating the path to practical, carbon-free electricity.
Conclusion
Diag2Diag represents a significant step in integrating artificial intelligence with experimental fusion science. By generating high-fidelity, synthetic diagnostic data from existing sensors, the approach promises more robust plasma control, reduced hardware complexity, and expanded possibilities for 24/7 fusion energy. As the field advances, AI will likely play a central role in turning laboratory insights into dependable, scalable power.
