Department of Energy Announces $29 Million for Research on Machine Learning, Artificial Intelligence, and Data Resources for Fusion Energy Sciences

Today, the U.S. Department of Energy (DOE) announced $29 million in funding for seven team awards for research in machine learning, artificial intelligence, and data resources for fusion energy sciences.

Nineteen institutions will apply advanced and autonomous algorithms to address high-priority research opportunities in fusion and plasma sciences. The teams include fusion and plasma researchers working in partnership with data and computational scientists through the establishment of multi-institutional, interdisciplinary collaborations.

“Artificial intelligence and scientific machine learning are transforming the way fusion and plasma research is conducted. These awards will advance a broad set of capabilities across the Fusion Energy Sciences (FES) program, making essential capabilities available for all stakeholders,” said Jean Paul Allain, DOE Associate Director of Science for Fusion Energy Sciences. “The U.S. is leveraging every tool in its pursuit of an aggressive program that will bring fusion energy to the grid on the most rapid timescale.”

The recipients will pursue research in science discovery, diagnostic data analysis, model extraction and reduction, plasma control, analysis of extreme-scale simulation data, and data-enhanced prediction. Multiple awards will focus on establishing new systems for managing, formatting, curating, and accessing experimental and simulation data with research products to be provided in publicly available databases.

The projects were selected by competitive peer review under the DOE Funding Opportunity Announcement for Machine Learning, Artificial Intelligence, and Data Resources for Fusion Energy Sciences.

Total funding is $29 million for projects lasting up to three years in duration, with $11 million in Fiscal Year 2023 dollars and outyear funding contingent on congressional appropriations. The list of projects and more information can be found on the FES program homepage.

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