  Next generation high performance (HP) tokamaks risk damage from unmitigated
disruptions at high current and power. Achieving reliable disruption prediction
for a device's HP operation based on its low performance (LP) data is key to
success. In this letter, through explorative data analysis and dedicated
numerical experiments on multiple existing tokamaks, we demonstrate how the
operational regimes of tokamaks can affect the power of a trained disruption
predictor. First, our results suggest data-driven disruption predictors trained
on abundant LP discharges work poorly on the HP regime of the same tokamak,
which is a consequence of the distinct distributions of the tightly correlated
signals related to disruptions in these two regimes. Second, we find that
matching operational parameters among tokamaks strongly improves cross-machine
accuracy which implies our model learns from the underlying scalings of
dimensionless physics parameters like q_{95}, \beta_{p} and confirms the
importance of these parameters in disruption physics and cross machine domain
matching from the data-driven perspective. Finally, our results show how in the
absence of HP data from the target devices, the best predictivity of the HP
regime for the target machine can be achieved by combining LP data from the
target with HP data from other machines. These results provide a possible
disruption predictor development strategy for next generation tokamaks, such as
ITER and SPARC, and highlight the importance of developing on existing machines
baseline scenario discharges of future tokamaks to collect more relevant
disruptive data.
