AI Could Enable Smaller, More Affordable Fusion Systems

The name Diag2Diag comes from “diagnostic,” the term for techniques used to analyze plasma, which rely on sensors to take measurements. Diagnostics typically record data at regular intervals—sometimes just fractions of a second apart—but some are too slow to capture rapidly evolving plasma instabilities, sudden changes that can make consistent power production difficult.

Figure 1. Princeton’s AI Breakthrough Tackles Key Fusion Power Challenge.

Fusion systems use many different diagnostics to monitor various plasma properties. One example is Thomson scattering, a technique employed in doughnut-shaped fusion devices called tokamaks. Thomson scattering measures the temperature and density of electrons—negatively charged particles—taking measurements quickly but still not fast enough to provide the detailed information plasma physicists need to maintain stability and optimal performance. Figure 1 shows Princeton’s AI Breakthrough Tackles Key Fusion Power Challenge.

Why the Plasma Edge Matters Most

The plasma edge, or pedestal, is the most critical part of the plasma to monitor—but also the hardest to measure. Thomson scattering is particularly valuable because many other diagnostics cannot measure the pedestal. Close monitoring of this region helps scientists optimize plasma performance, improving the efficiency of energy extraction from the fusion reaction.

For fusion energy to become a major part of the U.S. power system, it must be both economical and reliable. PPPL Staff Research Scientist SangKyeun Kim, part of the Diag2Diag research team, explained that AI advances bring the U.S. closer to these goals. “Today’s experimental tokamaks have many diagnostics, but future commercial systems will likely need far fewer,” Kim said. “Reducing components not directly involved in producing energy will help make reactors more compact. Fewer diagnostics also frees space inside the machine, simplifies the system, increases reliability, and lowers maintenance costs.”

AI Insights into Plasma Instabilities

The research team discovered that AI-generated data supports a leading theory about controlling edge-localized modes (ELMs)—bursts of energy that can damage a reactor’s inner walls. One promising ELM-suppression method involves resonant magnetic perturbations (RMPs), small adjustments to the magnetic fields holding the plasma. PPPL is at the forefront of ELM research, exploring both AI and traditional control methods.

A key theory suggests that RMPs create magnetic islands at the plasma edge, flattening the temperature and density across the pedestal. “Due to the limitations of the Thomson diagnostic, we cannot normally observe this flattening,” said PPPL Principal Research Scientist Qiming Hu, who also worked on the project. “Diag2Diag provided much more detail on how this happens and evolves.”

While magnetic islands can trigger ELMs, research indicates they can also be fine-tuned with RMPs to enhance plasma stability. Diag2Diag data confirmed the simultaneous flattening of temperature and density in the pedestal region, offering strong support for the magnetic island theory. Understanding this mechanism is essential for developing commercial fusion reactors.

Expanding AI Applications Beyond Fusion

The team is already planning to broaden Diag2Diag’s use. PPPL researcher Kolemen noted that many scientists are interested in applying the AI more widely. “Diag2Diag could be applied to other fusion diagnostics and even to fields where diagnostic data is missing or limited,” he said.

Source: SciTECHDaily

Cite this article:

Priyadharshini S (2025), Princeton’s Smart AI Cracks Major Challenge in Fusion Power, AnaTechMaz, pp.847