Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916854099869696 |
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| author | Sun, Mingyuan Fang, Zheng Wang, Jiaxu Zhang, Kunyi Zhang, Qiang Xu, Renjing |
| author_facet | Sun, Mingyuan Fang, Zheng Wang, Jiaxu Zhang, Kunyi Zhang, Qiang Xu, Renjing |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_15775 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity Sun, Mingyuan Fang, Zheng Wang, Jiaxu Zhang, Kunyi Zhang, Qiang Xu, Renjing General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Artificial Intelligence 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. |
| title | Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity |
| topic | General Relativity and Quantum Cosmology Instrumentation and Methods for Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15775 |