Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning
Abstract The tokamak offers a promising path to fusion energy, but disruptions pose a major economic risk, motivating solutions to manage their consequence. This work develops a reinforcement learning approach to this problem by training a policy to ramp-down the plasma current while avoiding limits...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-02146-6 |
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