🌎 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.
- "Nuclear fusion" in Nature Physics: A collection of articles on the state of nuclear fusion research. Available here.
Online Courses and Course MaterialsEdit
Conferences, Journals, and Professional OrganizationsEdit
Libraries and ToolsEdit
- "[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 OrganizationsEdit
- 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].