TLDR: A new AI tool named Diag2Diag, developed by a Princeton University-led team, is set to revolutionize fusion energy research. It reconstructs missing or incomplete plasma data with high precision, enabling more robust control and potentially leading to fusion reactors that are more compact, affordable, and reliable for continuous operation.
Princeton, NJ – A groundbreaking artificial intelligence (AI) tool, dubbed Diag2Diag, promises to transform the landscape of fusion energy research by providing an unprecedented view into the complex dynamics of plasma. Developed by an international collaborative team led by scientists at Princeton University, this innovative AI can reconstruct missing or incomplete plasma data with remarkable precision, a critical step towards making fusion power a viable and reliable energy source.
The core concept behind Diag2Diag is akin to an AI analyzing a silent movie to automatically restore its missing audio track. In the context of fusion, it takes data from various sensors within a fusion system and generates a synthetic, high-fidelity version of data for other types of sensors. This synthetic data not only aligns with real-world measurements but often surpasses the resolution capabilities of traditional physical sensors, particularly in challenging regions like the plasma’s edge, known as the pedestal.
Azarakhsh Jalalvand of Princeton University, the lead author of the paper recently published in Nature Communications, explained, “We have found a way to take the data from a bunch of sensors in a system and generate a synthetic version of the data for a different kind of sensor in that system.” This capability is crucial for enhancing the robustness of control systems while simultaneously reducing the complexity and cost of future fusion reactors. Jalalvand further noted that “Diag2Diag could also have applications in other systems such as spacecraft and robotic surgery by enhancing detail and recovering data from failing or degraded sensors, ensuring reliability in critical environments.”
The research is the fruit of an international collaboration involving Princeton University, the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), Chung-Ang University, Columbia University, and Seoul National University. The AI was trained using extensive sensor data gathered from experiments at the DIII-D National Fusion Facility, a premier DOE user facility dedicated to fusion research.
For fusion energy to transition from experimental devices to commercial power plants, continuous and uninterrupted operation is paramount. As Jalalvand highlighted, “Fusion devices today are all experimental laboratory machines, so if something happens to a sensor, the worst thing that can happen is that we lose time before we can restart the experiment. But if we are thinking about fusion as a source of energy, it needs to work 24/7, without interruption.” Diag2Diag addresses this by providing redundancy and resilience in plasma diagnostics, ensuring accurate data even if physical sensors degrade or fail.
Egemen Kolemen, principal investigator of the research, jointly appointed at PPPL and Princeton University, emphasized the economic benefits: “Diag2Diag is kind of giving your diagnostics a boost without spending hardware money.” This is particularly significant for diagnostics like Thomson scattering, which measures electron temperature and density but struggles with the rapid instabilities at the plasma’s critical edge. PPPL Staff Research Scientist SangKyeun Kim added, “Today’s experimental tokamaks have a lot of diagnostics, but future commercial systems will likely need to have far fewer. This will help make fusion reactors more compact by minimizing components not directly involved in producing energy.” Fewer diagnostics also translate to more robust, reliable systems with lower maintenance costs.
The AI has also provided deeper insights into controlling Edge-Localized Modes (ELMs), powerful energy bursts that can damage reactor walls. By generating detailed synthetic data, Diag2Diag supports the theory that resonant magnetic perturbations (RMPs)—small changes to magnetic fields—create ‘magnetic islands’ at the plasma edge, flattening temperature and density profiles and thereby stabilizing the plasma. PPPL Principal Research Scientist Qiming Hu stated, “Due to the limitation of the Thomson diagnostic, we cannot normally observe this flattening. Diag2Diag provided much more details on how this happens and how it evolves.” This understanding is vital for developing effective ELM suppression strategies for commercial reactors.
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The research received substantial support from the U.S. Department of Energy through multiple awards, the National Research Foundation of Korea, and the Princeton Laboratory for Artificial Intelligence, underscoring the growing recognition of AI’s role in advancing scientific frontiers. As fusion researchers continue to unravel the mysteries of plasma, AI-driven enhancements in diagnostics and control, exemplified by Diag2Diag, are poised to be crucial pillars in ushering in a new era of clean, limitless energy.
