Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations

arXiv:2401.05972v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv build construction cost cs.ce cs.lg data example focus inference low machine machine learning paper physics physics.comp-ph physics.plasm-ph plasma replace scientific simulations turbulence type