Machine learning is becoming an increasingly important tool in fusion research, allowing scientists to make new discoveries and improve fusion reaction efficiency. Researchers discussed the potential for using machine learning in fusion research at a recent workshop sponsored by the US Department of Energy, and identified several key areas for further study. One of the most difficult challenges in fusion research is accurately modeling and predicting the behavior of plasma, the superheated gas that powers fusion reactions. Traditional methods for simulating plasma rely on computationally intensive mathematical models, which can be difficult to solve and necessitate a significant amount of computational power. Machine learning algorithms, on the other hand, can be used to analyze large datasets and identify patterns and relationships that human experts would not be able to detect.

The US Department of Energy (DOE) has announced it will deploy $21 million (£16m) of funding for fusion energy research projects. The finance will enable scientists to take advantage of new artificial intelligence (AI) and machine learning technologies to speed up progress in fusion energy research. Part of the funding will also be allocated to improve operations at the Office of Science fusion facilities by automating data analysis and enabling algorithms. In a statement, DOE said: "AI and machine learning will help us to accelerate progress in fusion and keep American scientists at the forefront of fusion research."