Disruption prediction for future tokamaks using parameter-based transfer learning

Abstract Tokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device. Machine learning models have been widely used to predict incoming disruptions. However, future reactors, with much higher stored energy, cannot provide enough unmitigated disruption data at high performance to train the predictor before damaging themselves. Here we apply a deep parameter-based transfer learning method in disruption prediction. We train a model on the J-TEXT tokamak and transfer it, with only 20 discharges, to EAST, which has a large difference in size, operation regime, and configuration with respect to J-TEXT. Results demonstrate that the transfer learning method reaches a similar performance to the model trained directly with EAST using about 1900 discharge. Our results suggest that the proposed method can tackle the challenge in predicting disruptions for future tokamaks like ITER with knowledge learned from existing tokamaks.