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- Magnetic control of tokamak plasmas through deep reinforcement . . . - Nature
Tokamaks are torus-shaped devices for nuclear fusion research and are a leading candidate for the generation of sustainable electric power
- Accelerating fusion science through learned plasma control
In a paper published today in Nature, we describe how we can successfully control nuclear fusion plasma by building and running controllers on the Variable Configuration Tokamak (TCV) in Lausanne, Switzerland
- DeepMind uses AI to control plasma inside tokamak fusion reactor
Now, thanks to artificial intelligence firm DeepMind, fusion researchers are one step closer to extracting power from plasma hotter than the surface of the sun
- Swiss Plasma Center and DeepMind Use AI To Control . . . - SciTechDaily
“DeepMind was immediately interested in the prospect of testing their AI technology in a field such as nuclear fusion, and especially on a real-world system like a tokamak,” says Felici
- DeepMind AI: Accelerating Fusion Science Through Learned Plasma Control
In a paper published in Nature, DeepMind describes how it can successfully control nuclear fusion plasma by building and running controllers on the Variable Configuration Tokamak (TCV) in Lausanne, Switzerland
- This AI Can Control the Sun-Hot Plasma in a Nuclear Fusion Reactor
In new research published in the peer-reviewed journal Nature, the DeepMind team explains how they used deep reinforcement learning—a subfield of machine learning where a system can learn from
- Avoiding fusion plasma tearing instability with deep . . . - Nature
For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption
- Magnetic control of tokamak plasmas through deep reinforcement learning
We successfully produce and control a diverse set of plasma configurations on the Tokamak à Configuration Variable 1, 2, including elongated, conventional shapes, as well as advanced configurations, such as negative triangularity and ‘snowflake’ configurations
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