2005/05/16 by A. Asensio Ramos, H. Socas‐Navarro, H. Socas-Navarro · 1 citation
Mathematics · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Applied mathematics #Artificial intelligence #Artificial neural network #Biological system #Computer science #Generalization #Mathematical analysis #Mathematical optimization #Mathematics #Physics #Snapshot (computer storage) #Spectroscopy and Quantum Chemical Studies #Statistical physics #Stellar, planetary, and galactic studies #astro-ph
paper · pdf · doi:10.1051/0004-6361:20052865
8 pages, 5 figures, accepted for publication in A&A
arxiv created 2005/05/16 · openalex publication_date 2005/07/18 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A novel approach is presented for the solution of instantaneous chemical equilibrium problems. The chemical equilibrium can be considered, due to its intrinsically local character, as a mapping of the three-dimensional parameter space spanned by the temperature, hydrogen density and electron density into many one-dimensional spaces representing the number density of each species. We take advantage of the ability of artificial neural networks to approximate non-linear functions and construct neural networks for the fast and efficient solution of the chemical equilibrium problem in typical stellar atmosphere physical conditions. The neural network approach has the advantage of providing an analytic function, which can be rapidly evaluated. The networks are trained with a learning set (that covers the entire parameter space) until a relative error below 1% is reached. It has been verified that the networks are not overtrained by using an additional verification set. The networks are then applied to a snapshot of realistic three-dimensional convection simulations of the solar atmosphere showing good generalization properties.