Neural Rendering of the Wheeler-DeWitt Superposition: A Physics-Informed Machine Learning Approach to Multiversal Light-Cones

The intersection of quantum mechanics and general relativity yields the Wheeler-DeWitt equation (H|Psi> = 0), defining a timeless, uncollapsed universal wavefunction often theorized as the Multiverse. Observing this state requires a relativistic observer traveling at the speed of light (v=c), where proper time ceases and quantum decoherence halts. Traditionally, simulating such highly distorted, multi-layered spacetime geometries relies on computationally prohibitive brute-force ray-tracing. In this paper, we introduce a Physics-Informed Neural Network (PINN) architecture capable of bypassing standard trigonometric rendering. By training a deep multi-layer perceptron on 60,000 quantum coordinates spanning four distinct gravitational timelines, the network successfully learned the continuous geometry of the Wheeler-DeWitt superposition. Achieving a convergence physics loss of 0.0014, the AI instantly collapses multiple parallel light-cones into a single 2D relativistic viewport, rendering overlapping Einstein rings and a fractured Starbow. This methodology provides a high-speed computational tool for mapping non-linear multiversal geometries and solving inverse problems in observational astrophysics.