The Path to Safe Fusion Energy through Deep Learning

From Chris Cane May 16th, 2019

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San Diego Supercomputer Center Published on May 15, 2019 When the plasma inside a fusion reactor becomes unstable, there can be a release of energy that seriously damages the reactor. Key to preventing damage is predicting when these disruptions are about to happen. Professor William Tang of Princeton has successfully created a neural network that predicts these disruptions to a high level of accuracy, and was invited to give a talk at SDSC on his work. Tang's neural network is tuned specifically to predict disruptions, but the techniques used to train the neural network have crosscutting applications to other areas of scientific interest like cancer research. More about Professor Tang's research has been published in the journal Nature. Predicting disruptive instabilities in controlled fusion plasmas through deep learning https://www.nature.com/articles/s41586-019-1116-4 Category: Science & Technology

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The Path to Safe Fusion Energy through Deep Learning

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