Difference between revisions of "Nuclear Fusion"
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* [[Case Studies]] | * [[Case Studies]] | ||
| − | * [[Power (Management)]] | + | ** [[Power (Management)]] |
| + | ** [[Resources & Utilities]] | ||
* [[Energy]] Use with AI Algorithms | * [[Energy]] Use with AI Algorithms | ||
* [http://arxiv.org/abs/1811.00333 Applications of Deep Learning to Nuclear Fusion Research | Diogo R. Ferreira (on behalf of JET Contributors)] | * [http://arxiv.org/abs/1811.00333 Applications of Deep Learning to Nuclear Fusion Research | Diogo R. Ferreira (on behalf of JET Contributors)] | ||
Latest revision as of 19:01, 15 July 2020
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- Case Studies
- Energy Use with AI Algorithms
- Applications of Deep Learning to Nuclear Fusion Research | Diogo R. Ferreira (on behalf of JET Contributors)
- Can AI help crack the code of fusion power? | Rachel Becker - The Verge
Machine learning can help bring to Earth the clean fusion energy that lights the sun and stars. Researchers are using this form of artificial intelligence to create a model for rapid control of plasma -- the state of matter composed of free electrons and atomic nuclei, or ions -- that fuels fusion reactions. Machine learning speeds modeling of experiments aimed at capturing fusion energy on Earth | DOE/Princeton Plasma Physics Laboratory
