Today we're joined by Azarakhsh (Aza) Jalalvand, a research scholar at Princeton University, to discuss his work using deep reinforcement learning to control plasma instabilities in nuclear fusion reactors. Aza explains his team developed a model to detect and avoid a fatal plasma instability called ‘tearing mode’. Aza walks us through the process of collecting and pre-processing the complex diagnostic data from fusion experiments, training the models, and deploying the controller algorithm on the DIII-D fusion research reactor. He shares insights from developing the controller and discusses the future challenges and opportunities for AI in enabling stable and efficient fusion energy production.

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📖 CHAPTERS
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00:00 - Introduction
02:22 - How fusion works
05:48 - AI in fusion power
09:27 - Plasma instability
11:04 - How RL improves on classical control
21:26 - Building the simulator
28:48 - Training the model
39:32 - Did it work?
41:39 - Conclusion


🔗 LINKS & RESOURCES
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Avoiding fusion plasma tearing instability with deep reinforcement learning - https://www.nature.com/articles/s4158...
COMPASS Tokamak video -    • The first high-speed colour video fro...  
DOE Explains...Tokamaks - https://www.energy.gov/science/doe-ex...


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