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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| Summary: | 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 on a number of quantities correlated with disruptions. The policy training environment is a hybrid physics and machine learning model trained on simulations of the SPARC primary reference discharge (PRD) ramp-down, an upcoming burning plasma scenario which we use as a testbed. To address physics uncertainty and model inaccuracies, the simulation is massively parallelized on GPU with randomized physics parameters during policy training. The trained policy is then run in feedback on a transport simulator as a demonstration. We also directly address the crucial issue of control validation by demonstrating that a constraint-conditioned policy can be a trajectory design assistant that designs a library of feed-forward trajectories to handle different physics conditions and user constraint settings, a promising approach for the sensitive context of burning plasma tokamaks. Finally, we demonstrate that the training environment can be a useful platform for feed-forward optimization approaches by optimizing feed-forward trajectories that are robust to physics uncertainty. |
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| ISSN: | 2399-3650 |