[2604.06255] Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks

Astrophysics > Solar and Stellar Astrophysics arXiv:2604.06255 (astro-ph) [Submitted on 6 Apr 2026] Title:Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks Authors:Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage, Patrick Koller, Philipp M. Srivastava, Ugur Demir, Yongseok Jo, Almudena P. Marquez, Christoph Wuersch, Souvik Chakraborty, Vicky Kalogera, Aggelos Katsaggelos View a PDF of the paper titled Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks, by Manuel Ballester and 11 other authors View PDF HTML (experimental) Abstract:Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as \texttt{MESA} (Modules for Experiments in Stellar Astrophysics), which employ adaptive finite-difference methods, can become computationally expensive and challenging to scale for large stellar population synthesis ($>10^9$ stars). In this work, we present an self-supervised physics-informed neural network (PINN) framework that provides a mesh-free and fully differentiable approach to solving the stellar structure equations under hydrostatic and thermal equilibrium. The model takes as input the stellar boundary conditions (at the center and surface) together with the chemical composition, and learns continuous radial profiles for mass $M_r(r)$, pressure $P(r)$, density $\rho(r)$, temperature $T(r)$, and luminosity $L_r(r)$ by e...