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arXiv Machine Learning By Vishal Upendran, Carlos J. D\'iaz Baso

Machine Learning Applications in the Solar Interior and Atmosphere

Read the original on arXiv Machine Learning →

The article discusses how machine learning can be applied to the Sun’s interior and atmosphere, which are complex, multiscale dynamical systems. It introduces a state-space framework where the Sun’s global state vector is governed by thermodynamic, kinetic, and magnetic properties, and observations provide only partial, projected measurements. The authors propose that machine learning serves as a family of operators—Inference, Emulation, Propagation, and Control—to reconstruct, simulate, and couple this hidden state across different physical layers, thereby bridging observational gaps and accelerating nonlinear simulations to predict the Sun’s global evolution.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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