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We created the deep learning-dependent FFE neural network framework based on the comprehension of tokamak diagnostics and fundamental disruption physics. It can be confirmed a chance to extract disruption-associated designs efficiently. The FFE supplies a Basis to transfer the model to the focus on domain. Freeze & great-tune parameter-based mostly transfer Studying system is applied to transfer the J-TEXT pre-experienced model to a bigger-sized tokamak with A few goal data. The method enormously enhances the overall performance of predicting disruptions in long term tokamaks in contrast with other approaches, which includes occasion-primarily based transfer learning (mixing target and current details collectively). Expertise from current tokamaks is usually effectively placed on long term fusion reactor with unique configurations. Nevertheless, the method even now needs even more enhancement to generally be applied on to disruption prediction in upcoming tokamaks.
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The goal of this investigate is usually to Increase the disruption prediction overall performance on focus on tokamak with mainly awareness through the supply tokamak. The model general performance on focus on domain mainly is dependent upon the overall performance with the model while in the resource domain36. Consequently, we first require to get a large-general performance pre-skilled model with J-TEXT information.
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