Machine Learning Applications in the Solar Interior and Atmosphere
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 (S⃗) governed by thermodynamical, kinetic, and magnetic properties. Because solar observations represent projected, multi-modal, and partially observable measurements (O⃗[S⃗]) 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 (ℐ), Emulation (ℰ), Propagation (𝒫), and Control (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.
Related
- Frequency-Time Harmonization of Multi-Station Spectrograms for Machine Learning Applications in Solar Radio Burst Analysis.
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- Three-dimensional Magnetohydrodynamic Shocks and Associated Plasma Heating in the Solar Atmosphere
- Three-dimensional Radiative-magnetohydrodynamic Modeling of Particle-beam Heating of the Solar Atmosphere
BibTeX
@misc{upendran2026machine,
title = {{Machine Learning Applications in the Solar Interior and Atmosphere}},
author = {Vishal Upendran and Carlos J. Díaz Baso},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.34687},
eprint = {2610.34687},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.34687},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Machine Learning Applications in the Solar Interior and Atmosphere
AU - Vishal Upendran
AU - Carlos J. Díaz Baso
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.34687
UR - https://arxiv.org/abs/2610.34687
AB - 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 (S⃗) governed by thermodynamical, kinetic, and magnetic properties. Because solar observations represent projected, multi-modal, and partially observable measurements (O⃗[S⃗]) 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 (ℐ), Emulation (ℰ), Propagation (𝒫), and Control (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.
ER -