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Thursday, September 16, 2021

Better Fusion Power with AI

More moves towards fusion.

Can AI Make a Better Fusion Reactor? Nuclear physics may be one of machine learning's newest frontiers  Rebecca Sohn

Since the 1940s, physicists have tried, but no one has yet created an efficient nuclear fusion reaction. Meanwhile, AI and machine learning (ML) have, across many industries and applications, proved themselves quite capable at detecting subtle patterns in data that humans can't recognize. So could neural nets and the GPUs that power them help in nuclear fusion? The challenge, and it's a big one, would be to accelerate the worldwide quest to tame instabilities in hot plasmas and ultimately provide a source of sustainable, and carbon-free power.

"Physicists, they develop theoretical models, they write equations, they manipulate things mathematically," said Diogo Ferreira, a professor of information systems at the University of Lisbon's Instituto Superior TĂ©cnico in Portugal. "But there is a limit to that." AI, he says, can help.

Ferreira recently collaborated with colleagues working on the Joint European Torus (JET) in the UK in a study that detailed three different uses for AI, machine learning, and deep learning models for fusion research. Ferreira trained his models using diagnostic data from 48 sensors connected to the JET reactor, called bolometers, which collect power and radiation data.

One of Ferreira's models predicts disruptions in a super-hot plasma. In the study, he explains that depending on how it is trained, the model can either predict the likelihood of disruption—which can result in a plasma escaping confinement, jolting equipment, drastically reducing the plasma's temperature, and ending the reaction— or estimate the time at which that disruption will occur.

A second model detects anomalies in the plasma. Trained only on reactions where disruptions did not occur, the model can reproduce these "good" experiments. If the data originates in an experiment that ended in a disruption, the model can identify when and how the data diverges from that of a successful reaction. Scientists could use this process to better understand what ultimately leads to disruptions and eventually to run reactions in which disruptions are less likely.  ... 

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