A Cross-Domain Simulator and Benchmark for Uncertainty Quantification in Global 21-cm Inference

End-to-end uncertainty propagation in scientific inference often requires repeated evaluation of coupled simulators spanning multiple physical domains, often at prohibitive computational cost. Global 21-cm cosmology is an extreme example: a millikelvin cosmological signal must be separated from radio foregrounds and instrumental effects four orders of magnitude larger, making inference acutely sensitive to model misspecification. Reliable recovery is therefore contingent on an exceptionally accurate model that jointly propagates uncertainty through the complete observational chain. We present a differentiable, GPU-accelerated hybrid forward model combining neural emulation of the cosmological signal, a physics-informed multi-resolution parameterisation of the galactic foregrounds, and a linear surrogate model of the antenna. This unifies simulators spanning astrophysics and computational electromagnetics while reducing evaluation time by a factor of $7.2\times10^{7}$. Using GPU-accelerated Nested Slice Sampling, we benchmark the framework across 100 unseen signal realisations and find that 94 injected profiles lie entirely within their corresponding 95\% credible bands. Together, the simulator and reference Nested Slice Sampling results provide a demanding benchmark of posterior calibration, mode coverage, and evidence accuracy across next-generation stochastic samplers, simulation-based inference frameworks, and variational inference frameworks.