# Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks

arXiv: https://arxiv.org/abs/2506.10287 · HTML: https://mlsanity.com/paper/2506.10287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Offline Reinforcement Learning for Rotation Profile Control in Tokamaks](https://mlsanity.com/paper/2605.05857/) (2026)
- [Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark](https://mlsanity.com/paper/2606.07550/) (2026)
- [Bayesian Optimization with Rich Auxiliary Information via LLMs](https://mlsanity.com/paper/2609.19437/) (2026)

## Abstract (arXiv)

Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often affect dynamics beyond the experiment's duration. Existing tools like reinforcement learning, supervised learning, and Bayesian optimization address some of these challenges but fail to provide a comprehensive solution. To overcome these limitations, we present a multi-scale Bayesian optimization approach that integrates a high-frequency data-driven dynamics model with a low-frequency Gaussian process. By updating the Gaussian process between experiments, the method rapidly adapts to new data, refining the predictions of the less reliable dynamical model. We validate our approach by controlling tearing instabilities in the DIII-D nuclear fusion plant. Offline testing on historical data shows that our method significantly outperforms several baselines. Results on live experiments on the DIII-D tokamak, conducted under high-performance plasma scenarios prone to instabilities, shows a 50% success rate, marking a 117% improvement over historical outcomes.