arXiv:2607.04241v1 Announce Type: cross Abstract: Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible images provide complementary spatial cues including plasma deformation, local brightening, and radiation-structure evolution. Although the image modality improves the model's discriminative capability, it also substantially increases the computational cost during inference. To address this issue, we propose a hierarchical multi-to-single-modal knowledge distillation framework for disruption predi
This research is emerging as AI methodologies continue to be refined for complex scientific and industrial applications, and as the need for robust energy solutions intensifies.
Improving the efficiency and safety of fusion reactors like tokamaks can significantly accelerate the development of a sustainable energy source, impacting global energy security and climate goals.
The ability to integrate multi-modal data for disruption prediction more efficiently means that future fusion reactors could operate more reliably and safely with reduced computational overhead.
- · Fusion energy research institutions
- · Energy sector
- · AI hardware developers (for efficient inference)
- · Plasma physicists
- · Traditional energy providers
- · Inefficient AI model architectures
More accurate and computationally cheaper pre-emptive measures can be taken to prevent plasma disruptions in tokamaks.
Accelerated development and commercialization timelines for fusion reactors due to enhanced operational safety and predictability.
Reduced global reliance on fossil fuels as fusion energy becomes a viable and stable power source, leading to significant geopolitical shifts.
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Read at arXiv cs.AI