  Model-based plasma scenario development lies at the heart of the design and
operation of future fusion powerplants. Including turbulent transport in
integrated models is essential for delivering a successful roadmap towards
operation of ITER and the design of DEMO-class devices. Given the highly
iterative nature of integrated models, fast machine-learning-based surrogates
of turbulent transport are fundamental to fulfil the pressing need for faster
simulations opening up pulse design, optimization, and flight simulator
applications. A significant bottleneck is the generation of suitably large
training datasets covering a large volume in parameter space, which can be
prohibitively expensive to obtain for higher fidelity codes.
  In this work, we propose ADEPT (Active Deep Ensembles for Plasma Turbulence),
a physics-informed, two-stage Active Learning strategy to ease this challenge.
Active Learning queries a given model by means of an acquisition function that
identifies regions where additional data would improve the surrogate model. We
provide a benchmark study using available data from the literature for the
QuaLiKiz quasilinear transport model. We demonstrate quantitatively that the
physics-informed nature of the proposed workflow reduces the need to perform
simulations in stable regions of the parameter space, resulting in
significantly improved data efficiency. We show an up to a factor of 20
reduction in training dataset size needed to achieve the same performance as
random sampling. We then validate the surrogates on multichannel integrated
modelling of ITG-dominated JET scenarios and demonstrate that they recover the
performance of QuaLiKiz to better than 10\%. This matches the performance
obtained in previous work, but with two orders of magnitude fewer training data
points.
