

Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682
13 snips 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!
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Accidental Fusion
- Aza Jalalvand's entry into fusion research was accidental.
- A professor, needing help with data processing, introduced him to the field.
Fusion's Sun Replication
- Fusion reactors aim to replicate the sun's energy production on Earth.
- Containing the extreme heat required, 150 million degrees Celsius, is a challenge.
Fusion Safety
- Fusion is safer than fission; plasma cools down if control is lost.
- Fusion energy production is estimated to be a few decades away.