  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 using
only a few discharges. The first step is to use the existing understanding of
physics to extract physics-guided features from the diagnostic signals of each
tokamak, called physics-guided feature extraction (PGFE). The second step is to
align a few data from the future tokamak (target domain) and a large amount of
data from existing tokamak (source domain) based on a domain adaptation
algorithm called CORrelation ALignment (CORAL). It is the first attempt at
applying domain adaptation in the task of disruption prediction. PGFE has been
successfully applied in J-TEXT to predict disruption with excellent
performance. PGFE can also reduce the data volume requirements due to
extracting the less device-specific features, thereby establishing a solid
foundation for cross-tokamak disruption prediction. We have further improved
CORAL (supervised CORAL, S-CORAL) to enhance its appropriateness in feature
alignment for the disruption prediction task. To simulate the existing and
future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the
future tokamak, which has a large gap in the ranges of plasma parameters. The
utilization of the S-CORAL improves the disruption prediction performance on
future tokamak. Through interpretable analysis, we discovered that the learned
knowledge of the disruption prediction model through this approach exhibits
more similarities to the model trained on large data volumes of future tokamak.
