Princeton University: Princeton's AI reveals what fusion sensors can't see

A powerful new AI tool called Diag2Diag is revolutionizing fusion research by filling in missing plasma data with synthetic yet highly detailed information. Developed by Princeton scientists and international collaborators, this system uses sensor input to predict readings other diagnostics can't capture, especially in the crucial plasma edge region where stability determines performance. By reducing reliance on bulky hardware, it promises to make future fusion reactors more compact, affordable, and reliable.

The research is the result of an international collaboration between scientists at Princeton University, the U.S. Department of Energy's (DOE ) Princeton Plasma Physics Laboratory (PPPL), Chung-Ang University, Columbia University and Seoul National University. All of the sensor data used in the research to develop the AI was gathered from experiments at the DIII-D National Fusion Facility, a DOE user facility.

The new AI enhances the way scientists can monitor and control the plasma inside a fusion system and could help keep future commercial fusion systems a reliable source of electricity. "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," Jalalvand said.

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