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William & Mary to lead machine learning efforts for nuclear fusion
By: William & Mary
William & Mary is a leading contributor to this vision. W&M Associate Professor of Physics Saskia Mordijck is a principal investigator on an all-women-led project focusing on machine learning and AI for fusion, that has been selected for funding by the U.S. Department of Energy.
"Open and FAIR Fusion for Machine Learning Applications"
Collaborators include researchers at University of Wisconsin–Madison, MIT, Auburn University and The HDF Group.
Nuclear fusion could advance the federal government's goal to reach net-zero emissions by no later than 2050.
Machine learning has already been applied to nuclear fusion data sets both in the public and the private sector; however, these are not open and accessible.
Mordijck and her co-investigators will instead make their datasets findable, accessible, interoperable and reusable, building the first-ever platform for open and fair data in fusion energy research.
Sharing data for open fusion
There are several challenges in making nuclear fusion commercially viable, which many experts see as being 30 years away. Fusion reactions take place at a superheated state of matter called plasma.
Several plasma confinement strategies are currently being researched to successfully produce energy from fusion. AI-driven predictive modelling will be instrumental in optimizing plasma performance.
In order to be shared and used, however, data needs to be accessible and interoperable.
This is why the project brought together different universities, with their diverse machines, and an industry partner specializing in data structures.
Involving universities also means involving students in major developments. "We've already had some students involved in early work on that topic, and we hope to expand on that," said Mordijck.
Toward nuclear energy justice
For the next three years William & Mary will run a summer school focused on data science and machine learning for fusion energy.
According to Mordijck, combining data science with physics helps "diversify who actually gets to talk about fusion energy and who is involved in that research."
Securing environmental justice and providing "clean energy" jobs is part of the federal Justice40 initiative, which aims to deliver 40% of the overall benefits of relevant investments to disadvantaged communities.
Mordijck said that an important challenge was making sure that fusion energy could benefit the communities that need it the most.
Full release: https://news.wm.edu/
Antonella Di Marzio, Senior Research Writer
William & Mary