Elly Bayona | Searcharxiv

arXiv2026

Solving Einstein's Vacuum Equations with Physics-Informed Neural Networks: Boundary Conditions and Domain Decomposition

We investigate the application of Physics-Informed Neural Networks (PINNs) to the numerical solution of Einstein's vacuum field equations for static spacetimes. We first reproduce the Schwarzschild solution and then extend the method to the axisymmetric $q-$metric, a nontrivial exact solution characterized by a mass quadrupole moment. We analyze the influence of boundary conditions, domain decomposition, and equation redundancy on the convergence and stability of the training process. The proposed framework accurately reconstructs the metric functions in the computational domain while maintaining small residual errors. Our results demonstrate that PINNs provide a robust and flexible approach to solving Einstein's equations and offer a promising foundation for investigating gravitational configurations for which exact analytical solutions are unknown.

gr-qc↗

arXiv2024

Spherically symmetric collapse: Initial configurations

The initial state of the spherical gravitational collapse in general relativity has been studied with different methods, especially by using {\it a priori} given equations of state that describe the matter as a perfect fluid. We propose an alternative approach, in which the energy density of the perfect fluid is given as a polynomial function of the radial coordinate that is well-behaved everywhere inside the fluid. We then solve the corresponding differential equations, including the Tolman-Oppenheimer-Volkoff equilibrium condition, using a fourth-order Runge-Kutta method and obtain a consistent model with a central perfect-fluid core surrounded by dust. We analyze the Hamiltonian constraint, the mass-to-radius relation, the boundary and physical conditions, and the stability and convergence properties of the numerical solutions. The energy density and pressure of the resulting matter distribution satisfy the standard physical conditions. The model is also consistent with the Buchdahl limit and the speed of sound conditions, even by using realistic values of compact astrophysical objects such as neutron stars.

gr-qc↗