Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Authors: Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | 1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA 2Princeton University, Princeton, NJ, USA 3Princeton Plasma Physics Laboratory, Princeton, NJ, US. Correspondence to: Rohit Sonker <EMAIL>. |
| Pseudocode | No | The paper describes its methodology in natural language and presents a high-level pipeline diagram (Fig. 1) but does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper refers to using "historical data" and a "large dataset from past tokamak experiments" from the DIII-D Tokamak, specifically stating, "Our complete dataset consists of 15000 plasma trajectories from historical experiments at DIII-D Tokamak." However, no specific access information such as a link, DOI, or a citation to a publicly available version of this dataset is provided. |
| Dataset Splits | Yes | Early stopping is applied with a patience of 250 epochs based on performance on a validation set comprising 10% of the total data. |
| Hardware Specification | No | The paper mentions experiments conducted "at the DIII-D Tokamak" which is the system being controlled, but it does not specify any computing hardware (e.g., GPU/CPU models, memory specifications) used for running the experiments or training the models. |
| Software Dependencies | No | The paper mentions using "OMFIT software (Meneghini et al., 2015)" but does not provide a specific version number for it or any other software component used in the experiments. |
| Experiment Setup | Yes | We use the Adam optimizer with a learning rate of 3 10 4 and a weight decay of 1 10 3. Early stopping is applied with a patience of 250 epochs based on performance on a validation set comprising 10% of the total data. |