The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682
Apr 29, 2024
Aza Jalalvand, a research scholar at Princeton University, dives into the fascinating realm of using deep reinforcement learning to stabilize plasma in nuclear fusion reactors. He discusses the development of a model to combat the tearing mode instability while collecting complex data from fusion experiments. Aza highlights the critical role of machine learning in enhancing plasma understanding, the challenges of real-time data management, and the promising future of AI in clean energy production. Tune in for insights on the electrifying intersection of AI and fusion technology!
42:09
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Quick takeaways
- Deep reinforcement learning aids in controlling tearing mode plasma instability in fusion reactors.
- Machine learning integration enhances plasma stability by processing diverse experimental data types.
Deep dives
Introduction to Fusion Energy Production
Fusion energy production involves replicating the energy generation process of the sun on Earth. The main concept is to fuse atoms to release energy, similar to how the sun produces energy. However, creating a controlled fusion environment is complex due to the extreme heat required, around 150 million degrees Celsius, far surpassing the sun's temperature. The challenge lies in confining and controlling this intense heat within a donut-shaped vessel to enable stable fusion reactions.
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