Predicting Solar Wind Streams from the Inner-Heliosphere to Earth via Shifted Operator Inference

Abstract

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 outperforms a reduced-physics surrogate model, the Heliospheric Upwind Extrapolation model.

References in corpus (15)

• Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
• Origins of the Ambient Solar Wind: Implications for Space Weather
• Magnetic connectivity of the ecliptic plane within 0.5 AU : PFSS modeling of the first PSP encounter
• Data-driven reduced-order models via regularized operator inference for a single-injector combustion process
• Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
• Learning physics-based reduced-order models for a single-injector combustion process
• Lagrangian Dynamic Mode Decomposition for Construction of Reduced-Order Models of Advection-Dominated Phenomena
• The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations
• Bayesian operator inference for data-driven reduced-order modeling
• Dimensionality Reduction and Reduced Order Modeling for Traveling Wave Physics
• Using Parker Solar Probe observations during the first four perihelia to constrain global magnetohydrodynamic models
• Evaluation of CME Arrival Prediction Using Ensemble Modeling Based on Heliospheric Imaging Observations
• Forecasting the Ambient Solar Wind with Numerical Models. II. An Adaptive Prediction System for Specifying Solar Wind Speed Near the Sun
• Using gradient boosting regression to improve ambient solar wind model predictions
• A comparison study of extrapolation models and empirical relations in forecasting solar wind

Cited by in corpus (4)

• Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations
• Physics-informed active learning with simultaneous weak-form latent space dynamics identification
• Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method
• Physically consistent predictive reduced-order modeling by enhancing Operator Inference with state constraints