AI RESEARCH
Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks
arXiv CS.AI
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ArXi:2604.06255v1 Announce Type: cross 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