ZINZINBIN/Multimodal-Disruption-Prediction
Research-repository: Disruption Prediction and Analysis through Multimodal Deep Learning in KSTAR
This project helps fusion scientists and tokamak operators predict plasma disruptions in devices like KSTAR. By analyzing video footage of the plasma combined with numerical sensor data (like stored energy, plasma current, and electron temperature), it generates an early warning for potential disruptions. Researchers can use this to enhance the safety and efficiency of fusion energy experiments.
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Use this if you need to develop more accurate and timely disruption prediction systems for tokamak operations by leveraging both visual and numerical plasma data.
Not ideal if you are looking for a general-purpose machine learning framework or if your primary data source is not from fusion plasma experiments.
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Mar 07, 2025
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