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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| Format: | Article |
| Language: | English |
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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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| author | Allen M. Wang Cristina Rea Oswin So Charles Dawson Darren T. Garnier Chuchu Fan |
| author_facet | Allen M. Wang Cristina Rea Oswin So Charles Dawson Darren T. Garnier Chuchu Fan |
| author_sort | Allen M. Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ca3738deb254c2b99fa24a037449f55 |
| institution | Kabale University |
| issn | 2399-3650 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj-art-0ca3738deb254c2b99fa24a037449f552025-08-20T03:25:19ZengNature PortfolioCommunications Physics2399-36502025-06-018111310.1038/s42005-025-02146-6Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learningAllen M. Wang0Cristina Rea1Oswin So2Charles Dawson3Darren T. Garnier4Chuchu Fan5Plasma Science and Fusion Center, Massachusetts Institute of TechnologyPlasma Science and Fusion Center, Massachusetts Institute of TechnologyLaboratory for Information and Decision Systems, Massachusetts Institute of TechnologyLaboratory for Information and Decision Systems, Massachusetts Institute of TechnologyPlasma Science and Fusion Center, Massachusetts Institute of TechnologyLaboratory for Information and Decision Systems, Massachusetts Institute of TechnologyAbstract 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.https://doi.org/10.1038/s42005-025-02146-6 |
| spellingShingle | Allen M. Wang Cristina Rea Oswin So Charles Dawson Darren T. Garnier Chuchu Fan Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning Communications Physics |
| title | Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning |
| title_full | Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning |
| title_fullStr | Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning |
| title_full_unstemmed | Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning |
| title_short | Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning |
| title_sort | active ramp down control and trajectory design for tokamaks with neural differential equations and reinforcement learning |
| url | https://doi.org/10.1038/s42005-025-02146-6 |
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