Machine Learning Applications Enabling Fusion Energy: Recent Developments
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10894_2025_509_ReferencePDF.pdf
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91.44 KB
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Adobe PDF
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a3ffcb01d5c00d23ff63fa68a0aee042
Author(s)
Rea, Cristina
Date Issued
September 3, 2025
Journal
Journal of Fusion Energy
Publisher
Springer US
Citation
Rea, C. Machine Learning Applications Enabling Fusion Energy: Recent Developments. J Fusion Energ 44, 39 (2025).
Version
Author's final manuscript
Abstract
Over the last few years, machine learning helped to develop advanced capabilities for fusion energy over a broad range of domains. This includes advanced algorithms to extract information from fusion diagnostics, enhanced algorithms for plasma state estimation and control, accelerated simulation tools to improve predictive capabilities, and expanded modeling capabilities for fusion materials design. This topical collection covers recent developments in machine learning applied research further enabling the path to fusion energy; in particular it covers a wide breadth of fusion subfields – from inertial confinement fusion, to magnetically confined plasma, including high temperature superconducting magnet design and optimization. This editorial summarizes the collection while also providing a critical outlook on how machine learning can be used in the future to accelerate the development of fusion energy as a reliable energy source.
MIT Department
Massachusetts Institute of Technology. Plasma Science and Fusion Center
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10894-025-00509-z
