A neural-network surrogate for null geodesics enables faster black hole rendering with gravitational lensing, roughly 15x faster than an Euler-method baseline, but accuracy is measured against that same baseline.
Using physics-informed neural networks to compute quasinormal modes
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Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity
A neural-network surrogate for null geodesics enables faster black hole rendering with gravitational lensing, roughly 15x faster than an Euler-method baseline, but accuracy is measured against that same baseline.