2021/01/09 by Alan A. Kaptanoglu, Kyle Morgan, Kaptanoglu, Alan A. +6
Engineering · Physics and Astronomy · #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks #Plasma Physics (physics.plasm-ph) #Power System Optimization and Stability #physics.flu-dyn #physics.plasm-ph
paper · pdf · doi:10.48550/arxiv.2101.03436
arXiv admin note: text overlap with arXiv:2004.10389
arxiv created 2021/01/09 · openalex publication_date 2021/01/09 · arxiv updated 2021/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Plasmas are highly nonlinear and multi-scale, motivating a hierarchy of models to understand and describe their behavior. However, there is a scarcity of plasma models of lower fidelity than magnetohydrodynamics (MHD). Galerkin models, obtained by projection of the MHD equations onto a truncated modal basis, can furnish this gap in the lower levels of the model hierarchy. In the present work, we develop low-dimensional Galerkin plasma models which preserve global conservation laws by construction. This additional model structure enables physics-constrained machine learning algorithms that can discover these types of low-dimensional plasma models directly from data. This formulation relies on an energy-based inner product which takes into account all of the dynamic variables. The theoretical results here build a bridge to the extensive Galerkin literature in fluid mechanics, and facilitate the development of physics-constrained reduced-order models from plasma data.