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This paper focuses on the construction of non-intrusive Scientific MachineLearning (SciML) Reduced-Order Models (ROMs) for nonlinear, chaotic plasmaturbulence simulations. In particular, we propose using Operator Inference(OpInf) to build low-cost physics-based ROMs from data for such simulations. Asa representative example, we focus on the Hasegawa-Wakatani (HW) equations usedfor modeling two-dimensional electrostatic drift-wave plasma turbulence. For acomprehensive perspective of the potential of OpInf to construct accurate ROMsfor this model, we consider a setup for the HW equations that leads to theformation of complex, nonlinear, and self-driven dynamics, and perform two setsof experiments. We first use the data obtained via a direct numericalsimulation of the HW equations starting from a specific initial condition andtrain OpInf ROMs for predictions beyond the training time horizon. In thesecond, more challenging set of experiments, we train ROMs using the samedataset as before but this time perform predictions for six other initialconditions. Our results show that the OpInf ROMs capture the important featuresof the turbulent dynamics and generalize to new and unseen initial conditionswhile reducing the evaluation time of the high-fidelity model by up to fiveorders of magnitude in single-core performance. In the broader context offusion research, this shows that non-intrusive SciML ROMs have the potential todrastically accelerate numerical studies, which can ultimately enable taskssuch as the design and real-time control of optimized fusion devices.

Learning physics-based reduced models from data for the Hasegawa-Wakatani equations