Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks

Mon, 06 Apr 2026 00:00:00 +0000

Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage, Patrick Koller, Philipp M Srivastava, Ugur Demir, Yongseok Jo, Almudena P Marquez, Christoph Würsch, Souvik Chakraborty, Vicky Kalogera, and Aggelos K. Katsaggelos

Abstract

This research introduces a mesh-free approach to solving the fundamental equations of stellar structure using Physics-Informed Neural Networks (PINNs). By enforcing hydrostatic and thermal equilibrium constraints directly within the loss function, we eliminate the need for traditional grid-based discretization used in solvers like MESA.

Our framework incorporates auxiliary neural surrogates for microphysics (opacities and equation of state), allowing the model to learn continuous, differentiable radial profiles across a wide range of stellar masses.

BibTeX Citation

@misc{ballester2026learningstellarstructure,
 title={Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks},
 author={Manuel Ballester and Santiago Lopez-Tapia and Seth Gossage and Patrick Koller and Philipp M. Srivastava and Ugur Demir and Yongseok Jo and Almudena P. Marquez and Christoph Wuersch and Souvik Chakraborty and Vicky Kalogera and Aggelos Katsaggelos},
 year={2026},
 eprint={2604.06255},
 archivePrefix={arXiv},
 primaryClass={astro-ph.SR},
 url={https://arxiv.org/abs/2604.06255},
}

Equation of State | Patrick Koller

Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks

Abstract

BibTeX Citation