# Cross-tokamak Disruption Prediction based on Physics-Guided Feature Extraction and domain adaptation

arXiv: https://arxiv.org/abs/2309.05361 · HTML: https://mlsanity.com/paper/2309.05361/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 11 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [Hybrid deep learning architecture for general disruption prediction across tokamaks](https://mlsanity.com/paper/2007.01401/) (2020)
- [DART: Dropouts meet Multiple Additive Regression Trees](https://mlsanity.com/paper/1505.01866/) (2015)
- [Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach](https://mlsanity.com/paper/1912.01166/) (2019)
- [Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks](https://mlsanity.com/paper/2109.08956/) (2021)
- [IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction](https://mlsanity.com/paper/2208.13197/) (2022)
- [Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor](https://mlsanity.com/paper/2208.09594/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST](https://mlsanity.com/paper/2607.04241/) (2026)

## Abstract (arXiv)

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak using only a few discharges. The first step is to use the existing understanding of physics to extract physics-guided features from the diagnostic signals of each tokamak, called physics-guided feature extraction (PGFE). The second step is to align a few data from the future tokamak (target domain) and a large amount of data from existing tokamak (source domain) based on a domain adaptation algorithm called CORrelation ALignment (CORAL). It is the first attempt at applying domain adaptation in the task of disruption prediction. PGFE has been successfully applied in J-TEXT to predict disruption with excellent performance. PGFE can also reduce the data volume requirements due to extracting the less device-specific features, thereby establishing a solid foundation for cross-tokamak disruption prediction. We have further improved CORAL (supervised CORAL, S-CORAL) to enhance its appropriateness in feature alignment for the disruption prediction task. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak, which has a large gap in the ranges of plasma parameters. The utilization of the S-CORAL improves the disruption prediction performance on future tokamak. Through interpretable analysis, we discovered that the learned knowledge of the disruption prediction model through this approach exhibits more similarities to the model trained on large data volumes of future tokamak.