Summary of Time Series Viewmakers For Robust Disruption Prediction, by Dhruva Chayapathy et al.
Time Series Viewmakers for Robust Disruption Prediction
by Dhruva Chayapathy, Tavis Siebert, Lucas Spangher, Akshata Kishore Moharir, Om Manoj Patil, Cristina Rea
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine Learning guided data augmentation may support the development of technologies in the physical sciences, such as nuclear fusion tokamaks. The paper studies 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 struggle to generalize across different machines. This limits the effectiveness of ML models across different tokamak designs and operating conditions. The paper explores the use of a novel time series viewmaker network to generate diverse augmentations or “views” of training data, showing that incorporating views during training improves AUC and F2 scores on DisruptionBench tasks compared to standard or no augmentations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can help detect problems in nuclear fusion reactors. The problem is that these models only work well for specific machines, but not for different ones. To solve this, scientists use a new way to look at data, called time series viewmaker networks. This helps the models learn more about disruptions and how to avoid them. By using this method, the models get better at predicting when problems will happen. |
Keywords
» Artificial intelligence » Auc » Data augmentation » Machine learning » Time series
