Time Series Viewmakers for Robust Disruption Prediction

TL;DR

This paper introduces a novel time series viewmaker network for data augmentation to improve machine learning models' ability to predict plasma disruptions in tokamaks, enhancing robustness and generalization across different fusion devices.

Contribution

The study proposes a new time series viewmaker network for generating diverse data augmentations, significantly improving disruption prediction performance across various tokamak configurations.

Findings

Improved AUC and F2 scores with viewmaker-based augmentation

Enhanced model robustness and generalization across tokamaks

Potential for broader application in fusion energy prediction

Abstract

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.