In the dynamic realm of fusion research, where every moment is crucial, scientists face the challenge of swiftly analyzing vast amounts of data to make informed decisions between experimental runs. At the DIII-D National Fusion Facility in San Diego, researchers have a mere 10-minute window to troubleshoot, adjust, and prepare for the next fusion shot.
Joseph Abbate, a Ph.D. candidate at Princeton, emphasized the urgency, stating, "You have to make a lot of decisions in a very short amount of time." To address this challenge, a collaborative effort between Princeton University, Carnegie Mellon University, and MIT has leveraged large language models, akin to ChatGPT and others, to streamline data analysis for fusion researchers.
The researchers applied these language models to quickly sift through extensive data, identifying patterns from previous experiments, providing insights into control systems, and promptly answering questions about fusion reactors and plasma physics. According to Viraj Mehta of Carnegie Mellon University, fusion research presents an ideal scenario for applying large language models due to the wealth of available information and the need for quick access.
The project originated during a graduate student-led hackathon at Princeton, where the team enhanced existing language models through a process called retrieval-augmented generation. This technique supplements the model's general dataset with specific information, in this case, a database including shot logs and notes from DIII-D experiments that aren't publicly available on the internet.
Abbate highlighted the collaborative coding effort, saying, "It's like having another helper always with you in the room who knows about every fusion trial that's taken place at the reactor and can provide recommendations based on what's happened in the past."
The model's application goes beyond real-time experimentation, extending to retired fusion reactors. Allen Wang from MIT demonstrated the adaptability of the framework for Alcator C-Mod, a fusion reactor that ceased operations in 2016. This capability allows scientists to retrieve valuable information from reactors that have been inactive for decades, potentially aiding in addressing challenges with future reactors.
Egemen Kolemen, the senior author and associate professor, highlighted the potential impact, stating, "By gathering all the text data and plugging them into a language model, we might relearn some key information that can help us solve an issue we face with future reactors."
As the researchers continue to refine their model, there are plans to implement it at DIII-D and other fusion facilities. This collaborative effort not only enhances decision-making during experiments but also serves as a valuable tool for the next generation of fusion researchers, providing easy access to a wealth of information for more efficient problem-solving.
