Nuclear Fusion: Difference between revisions
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Nuclear fusion reactors have the potential to produce safe and carbon-free electricity using a virtually limitless hydrogen fuel supply, but currently consume more energy than they produce.<ref>{{Cite journal|last=Cowley|first=Steven C.|date=2016-05|title=The quest for fusion power|url=http://dx.doi.org/10.1038/nphys3719|journal=Nature Physics|volume=12|issue=5|pages=384–386|doi=10.1038/nphys3719|issn=1745-2473}}</ref> While considerable scientific and engineering research is still needed, machine learning can help accelerate this work by guiding experimental design and monitoring physical processes. For instance, machine learning can help detect plasma disruptions during fusion experiments and prioritize which parameter configurations to explore.<ref name=":0">Rolnick, David, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross et al. "Tackling climate change with machine learning." ''arXiv preprint arXiv:1906.05433'' (2019).</ref> |
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Nuclear fusion has the potential to produce safe, carbon-free electricity, but such reactors continue to consume more energy than they produce. While basic science and engineering are still needed, ML can help inform nuclear fusion research in a variety of ways, e.g., by suggesting parameters for physical experiments or modeling the behavior of plasma inside reactors. |
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==Background Readings== |
==Background Readings== |
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*'''"Nuclear fusion" in Nature Physics''': A collection of articles on the state of nuclear fusion research. Available [https://www.nature.com/collections/bccqhmkbyw/ here]. |
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==Online Courses and Course Materials== |
==Online Courses and Course Materials== |
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==Community== |
==Community== |
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==Libraries and Tools== |
==Libraries and Tools == |
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==Data== |
==Data== |
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==Future Directions== |
==Future Directions== |
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*"[S]peculatively, ML could help characterize this evolution and even help steer plasma into safe states through reactor control. ML models for such fusion applications would likely employ a combination of simulated and experimental data, and would need to account for the different physical characteristics, data volumes, and simulator speeds or accuracies associated with different reactor types."<ref name=":0" /> |
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Revision as of 21:17, 6 December 2020
🌎 This article is a stub, and is currently under construction. You can help by adding to it!
This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
Nuclear fusion has the potential to produce safe, carbon-free electricity, but such reactors continue to consume more energy than they produce. While basic science and engineering are still needed, ML can help inform nuclear fusion research in a variety of ways, e.g., by suggesting parameters for physical experiments or modeling the behavior of plasma inside reactors.
Background Readings
- "Nuclear fusion" in Nature Physics: A collection of articles on the state of nuclear fusion research. Available here.
Online Courses and Course Materials
Community
Libraries and Tools
Data
Future Directions
- "[S]peculatively, ML could help characterize this evolution and even help steer plasma into safe states through reactor control. ML models for such fusion applications would likely employ a combination of simulated and experimental data, and would need to account for the different physical characteristics, data volumes, and simulator speeds or accuracies associated with different reactor types."[1]
References
- ↑ Cite error: Invalid
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