50%) will neither exploit the minimal info from EAST nor the overall knowledge from J-TEXT. One particular possible explanation would be that the EAST discharges are certainly not consultant enough and the architecture is flooded with J-TEXT information. Scenario 4 is qualified with twenty EAST discharges (ten disruptive) from scratch. To stay away from in excess of-parameterization when schooling, we utilized L1 and L2 regularization into the model, and modified the learning charge plan (see Overfitting managing in Techniques). The functionality (BA�? sixty.28%) implies that making use of only the minimal info from the focus on domain is just not enough for extracting standard functions of disruption. Circumstance 5 works by using the pre-trained design from J-Textual content immediately (BA�? fifty nine.44%). Using the source model along would make the general understanding about disruption be contaminated by other information particular towards the source domain. To conclude, the freeze & fine-tune method has the capacity to attain a similar overall performance using only twenty discharges with the full details baseline, and outperforms all other situations by a big margin. Employing parameter-based transfer learning procedure to mix both equally the supply tokamak design and details through the target tokamak properly may well help make far better use of data from both domains.
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The configuration and Procedure routine gap involving J-TEXT and EAST is much bigger compared to the hole concerning Those people ITER-like configuration tokamaks. Facts and effects with regards to the numerical experiments are revealed in Desk 2.
Raw information were being created with the J-TEXT and EAST facilities. Derived details can be found from your corresponding creator on fair ask for.
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