Solving Lane-Emden-Type Eigenvalue Problems with Physics-Informed Neural Networks
- 18 November 2025
- journal article
- research article
- Published by Springer Nature in International Journal of Theoretical Physics
- Vol. 64 (12) , 328
Abstract
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This publication has 19 references indexed in Scilit:
- GEPINN: An innovative hybrid method for a symbolic solution to the Lane–Emden type equation based on grammatical evolution and physics-informed neural networksAstronomy and Computing, 2024
- A novel numerical solution to lane-emden type equations using monte carlo techniquePhysica Scripta, 2023
- A class of improved fractional physics informed neural networksNeurocomputing, 2023
- Modelling Lane–Emden type equations using Physics-Informed Neural NetworksAstronomy and Computing, 2023
- Monte Carlo simulation of Lane–Emden type equations arising in astrophysicsAstronomy and Computing, 2022
- A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equationsJournal of Computational Physics, 2022
- Physics-informed machine learningNature Reviews Physics, 2021
- The Lane-Emden equation with variable double-phase and multiple regimeProceedings of the American Mathematical Society, 2020
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equationsJournal of Computational Physics, 2019
- A reliable algorithm for the approximate solution of the nonlinear Lane‐Emden type equations arising in astrophysicsNumerical Methods for Partial Differential Equations, 2017