- Aug 20, 2021 3:57 pm GMT
"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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