Solving Einstein equations using deep learning

Author:

Zhi-Han Li, Chen-Qi Li, Long-Gang Pang

Keyword:

General Relativity and Quantum Cosmology, General Relativity and Quantum Cosmology (gr-qc), Nuclear Theory (nucl-th), Computational Physics (physics.comp-ph)

journal:

date:

2023-09-13 16:00:00

Abstract

Einstein field equations are notoriously challenging to solve due to their complex mathematical form, with few analytical solutions available in the absence of highly symmetric systems or ideal matter distribution. However, accurate solutions are crucial, particularly in systems with strong gravitational field such as black holes or neutron stars. In this work, we use neural networks and auto differentiation to solve the Einstein field equations numerically inspired by the idea of physics-informed neural networks (PINNs). By utilizing these techniques, we successfully obtain the Schwarzschild metric and the charged Schwarzschild metric given the energy-momentum tensor of matter. This innovative method could open up a different way for solving space-time coupled Einstein field equations and become an integral part of numerical relativity.

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