Greifswald 2024 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
P: Fachverband Plasmaphysik
P 11: Codes and Modeling I
P 11.2: Vortrag
Dienstag, 27. Februar 2024, 17:00–17:25, ELP 6: HS 3
Learning physics-based reduced models from data for the Hasegawa-Wakatani equations — •Constantin Gahr1, Ionuţ-Gabriel Farcaş2, and Frank Jenko1 — 1Max-Planck-Institute for Plasma Physics, 85748 Garching, DE — 2Oden Institute for Computational Engineering & Sciences, Austin, TX 78712, US
This presentation focuses on the construction of non-intrusive Scientific Machine-Learning (SciML) Reduced Order Models (ROMs) for nonlinear, chaotic 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 plasma turbulence. We first use the data obtained via a direct numerical simulation of the HW equations starting from a specific initial condition and train OpInf ROMs for predictions beyond the training time horizon. In the second, more challenging set of experiments, we train ROMs using the same data set as before but this time perform predictions for six other initial conditions. Our results show that the OpInf ROMs capture the important features of the turbulent dynamics and generalize to new and unseen initial conditions while reducing the evaluation time of the high-fidelity model by up to six orders of magnitude in single-core performance. 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 and real-time control of optimized fusion devices.
Keywords: Hasegawa Wakatani; Operator Inference; Reduced Order Model