Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity

TL;DR

This paper introduces GravLensX, a neural network-based method for efficiently rendering black holes with gravitational lensing effects, significantly reducing computational time while maintaining accuracy.

Contribution

It presents a novel neural network approach to simulate gravitational lensing around black holes, enabling faster and scalable visualizations compared to traditional techniques.

Findings

Achieves 15x faster rendering times.

Accurately models gravitational lensing effects.

Demonstrates scalability to multiple black hole systems.

Abstract

We present GravLensX, an innovative method for rendering black holes with gravitational lensing effects using neural networks. The methodology involves training neural networks to fit the spacetime around black holes and then employing these trained models to generate the path of light rays affected by gravitational lensing. This enables efficient and scalable simulations of black holes with optically thin accretion disks, significantly decreasing the time required for rendering compared to traditional methods. We validate our approach through extensive rendering of multiple black hole systems with superposed Kerr metric, demonstrating its capability to produce accurate visualizations with significantly $15\times$ reduced computational time. Our findings suggest that neural networks offer a promising alternative for rendering complex astrophysical phenomena, potentially paving a new path to astronomical visualization.