Machine learning helps predict and simulate solar activity patterns
Machine Learning Applications in the Solar Interior and Atmosphere
Machine Learning
Summary
The Sun’s inside and outside are connected and change in complicated ways over time. Solar scientists need to understand many things happening at once, but direct measurements are limited and incomplete. The authors explain how machine learning can act like mathematical tools that fill in missing information, speed up simulations, and help track the Sun’s behavior more accurately. This approach combines data and physics-based models to improve the ability to predict how the Sun evolves.
What this means in practice
- •For space weather forecasters: Use machine learning operators to enhance model predictions by reconstructing hidden solar states from sparse observational data for better forecasting of solar events.
- •For planetary climate modelers: Incorporate accelerated simulations of solar magnetic and thermodynamic interactions to improve boundary conditions influencing planetary atmospheres.
A position paper. It proposes an approach and reports no results.
Authors
Vishal Upendran, Carlos J. Díaz Baso
Abstract
The solar interior and atmosphere constitute a strongly coupled, multiscale dynamical system defined by continuous temporal evolution. Predicting the state of this system requires modeling physical interactions that span disparate spatial and temporal scales, constrained by heterogeneous and sparse data. This chapter formalizes the Sun within the mathematical framework of dynamical systems theory, defining a global state vector ($\vec{S}$) governed by thermodynamical, kinetic, and magnetic properties. Because solar observations represent projected, multi-modal, and partially observable measurements ($\vec{O}[\vec{S}]$) of this hidden state, pure physics-based deduction is fundamentally constrained. We present a unifying thesis: the primary role of machine learning in heliophysics is to act as a structured family of mathematical operators---specifically Inference ($\mathcal{I}$), Emulation ($\mathcal{E}$), Propagation ($\mathcal{P}$), and Control ($\mathcal{C}$)---that reconstruct, simulate, and couple this global state vector across disparate physical layers. By framing the vast, multi-disciplinary literature within this unified state-space taxonomy, we demonstrate how data-driven techniques bridge observational gaps to resolve hidden multiscale couplings and dramatically accelerate the simulation of non-linear processes, establishing a rigorous pathway to predict the global evolution of the Sun.