Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference

• Published in: Journal of computational physics 2023-01, Vol.473, p.111689, Article 111689
• Main Authors: Issan, Opal, Kramer, Boris
• Format: Article
• Language: English
• Subjects: Data-driven model reduction; Magnetohydrodynamics; Operator inference; Scientific machine learning; Solar wind modeling; Space weather prediction
• ISSN: 0021-9991; 1090-2716
• DOI: 10.1016/j.jcp.2022.111689

•We propose a new strategy for efficient data-driven heliospheric solar wind modeling.•The proposed method generalizes to a wide class of advection-dominated phenomena.•This work extends the operator inference method to exploit translational symmetries.•We showcase the performance of our method on 2D and 3D magnetohydrodynamic models. Solar wind conditions are predominantly predicted via three-dimensional numerical magnetohydrodynamic (MHD) models. Despite their ability to produce highly accurate predictions, MHD models require computationally intensive high-dimensional simulations. This renders them inadequate for making time-sensitive predictions and for large-ensemble analysis required in uncertainty quantification. This paper presents a new data-driven reduced-order model (ROM) capability for forecasting heliospheric solar wind speeds. Traditional model reduction methods based on Galerkin projection have difficulties with advection-dominated systems—such as solar winds—since they require a large number of basis functions and can become unstable. A core contribution of this work addresses this challenge by extending the non-intrusive operator inference ROM framework to exploit the translational symmetries present in the solar wind caused by the Sun's rotation. The numerical results show that our method can adequately emulate the MHD simulations and is more accurate than a reduced-physics surrogate model, the Heliospheric Upwind Extrapolation model.