  This paper focuses on the construction of non-intrusive Scientific Machine
Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations.
In particular, we propose using Operator Inference (OpInf) to build low-cost
physics-based ROMs from data for such simulations. As a representative example,
we focus on the Hasegawa-Wakatani (HW) equations used for modeling
two-dimensional electrostatic drift-wave turbulence. For a comprehensive
perspective of the potential of OpInf to construct accurate ROMs, we consider
three setups for the HW equations by varying a key model parameter, namely the
adiabaticity coefficient. These setups lead to the formation of complex and
nonlinear dynamics, which makes the construction of accurate ROMs of any kind
challenging. We generate the training datasets by performing direct numerical
simulations of the HW equations and recording the computed state data and
outputs the over a time horizon of 100 time units in the turbulent phase. We
then use these datasets to construct OpInf ROMs for predictions over 400
additional time units. Our results show that the OpInf ROMs capture the
important features of the turbulent dynamics and generalize beyond the training
time horizon while reducing the computational effort of the high-fidelity
simulation by up to five orders of magnitude. In the broader context of fusion
research, this shows that non-intrusive SciML ROMs have the potential to
drastically accelerate numerical studies, which can ultimately enable tasks
such as the design of optimized fusion devices.
