1 research outputs found
Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation
The high acquisition cost and the significant demand for disruptive
discharges for data-driven disruption prediction models in future tokamaks pose
an inherent contradiction in disruption prediction research. In this paper, we
demonstrated a novel approach to predict disruption in a future tokamak only
using a few discharges based on a domain adaptation algorithm called CORAL. It
is the first attempt at applying domain adaptation in the disruption prediction
task. In this paper, this disruption prediction approach aligns a few data from
the future tokamak (target domain) and a large amount of data from the existing
tokamak (source domain) to train a machine learning model in the existing
tokamak. To simulate the existing and future tokamak case, we selected J-TEXT
as the existing tokamak and EAST as the future tokamak. To simulate the lack of
disruptive data in future tokamak, we only selected 100 non-disruptive
discharges and 10 disruptive discharges from EAST as the target domain training
data. We have improved CORAL to make it more suitable for the disruption
prediction task, called supervised CORAL. Compared to the model trained by
mixing data from the two tokamaks, the supervised CORAL model can enhance the
disruption prediction performance for future tokamaks (AUC value from 0.764 to
0.890). Through interpretable analysis, we discovered that using the supervised
CORAL enables the transformation of data distribution to be more similar to
future tokamak. An assessment method for evaluating whether a model has learned
a trend of similar features is designed based on SHAP analysis. It demonstrates
that the supervised CORAL model exhibits more similarities to the model trained
on large data sizes of EAST. FTDP provides a light, interpretable, and
few-data-required way by aligning features to predict disruption using small
data sizes from the future tokamak.Comment: 15 pages, 9 figure