Time Series Viewmakers for Robust Disruption Prediction
This addresses the critical barrier of scaling fusion technology by enhancing model robustness for disruption avoidance, though it appears incremental as it builds on existing data augmentation methods.
The study tackled the problem of machine learning models struggling to generalize across different tokamak designs for disruption prediction in nuclear fusion, and found that using a novel time series viewmaker network for data augmentation improved AUC and F2 scores on DisruptionBench tasks.
Machine Learning guided data augmentation may support the development of technologies in the physical sciences, such as nuclear fusion tokamaks. Here we endeavor to study the problem of detecting disruptions i.e. plasma instabilities that can cause significant damages, impairing the reliability and efficiency required for their real world viability. Machine learning (ML) prediction models have shown promise in detecting disruptions for specific tokamaks, but they often struggle in generalizing to the diverse characteristics and dynamics of different machines. This limits the effectiveness of ML models across different tokamak designs and operating conditions, which is a critical barrier to scaling fusion technology. Given the success of data augmentation in improving model robustness and generalizability in other fields, this study explores the use of a novel time series viewmaker network to generate diverse augmentations or "views" of training data. Our results show that incorporating views during training improves AUC and F2 scores on DisruptionBench tasks compared to standard or no augmentations. This approach represents a promising step towards developing more broadly applicable ML models for disruption avoidance, which is essential for advancing fusion technology and, ultimately, addressing climate change through reliable and sustainable energy production.