Shahid Beheshti University Computational Mathematics and Computer Modeling with Applications (CMCMA) 2783-4859 5 1 2026 04 01 A Novel Hybrid Architecture Combining High-Order B-Splines and Physics-Informed Neural Networks for Solving an Astrophysical Model 1 14 106946 10.48308/CMCMA.5.1.1 EN Sima Naraghi Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran Kourosh Parand Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran Journal Article 2026 01 30 In this paper, we present a novel architecture for approximating solutions to differential equations in astrophysics. Our approach introduces the innovative use of nonlinear B-spline basis functions as activation functions within a neural network. Furthermore, we develop a physics-informed B-spline neural network framework with associated control points to address the Lane--Emden equations, frequently encountered in astronomy. This new method offers enhanced accuracy while requiring fewer epochs than conventional neural networks. Physics-Informed Neural Networks High-Order B-Spline Astrophysical Modeling {Lane--Emden equations} Numerical Methods https://cmcma.sbu.ac.ir/article_106946_6507053b723f565e3a0fb47d5b99d204.pdf