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This page is about the applications of machine learning (ML) in the context of nuclear nusion. For an overview of nuclear nusion more generally, please see the Wikipedia page on this topic.
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[edit | edit source]
- "Nuclear fusion" in Nature Physics: A collection of articles on the state of nuclear fusion research. Available here.
Online Courses and Course Materials[edit | edit source]
Conferences, Journals, and Professional Organizations[edit | edit source]
Libraries and Tools[edit | edit source]
Data[edit | edit source]
Future Directions[edit | edit source]
- "[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."
Relevant Groups and Organizations[edit | edit source]
References[edit | edit source]
- Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].