Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity
This provides an efficient method for astronomical visualization of black holes, though it is incremental as it applies existing neural network techniques to a specific domain.
The paper tackles the problem of rendering black holes with gravitational lensing effects by training neural networks to fit spacetime and generate light ray paths, achieving a 15× reduction in computational time compared to traditional methods.
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.