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We intended the deep learning-dependent FFE neural network framework determined by the understanding of tokamak diagnostics and fundamental disruption physics. It is demonstrated a chance to extract disruption-connected designs successfully. The FFE gives a foundation to transfer the design to your goal domain. Freeze & good-tune parameter-based tr
This will make them not contribute to predicting disruptions on long term tokamak with another time scale. On the other hand, even more discoveries within the physical mechanisms in plasma physics could perhaps add to scaling a normalized time scale throughout tokamaks. We can attain an even better method to procedure signals in a larger time scale