We explore how effort.jl turns petabytes of cosmology data into fast, trustworthy inferences. A fast neural-network surrogate and physics-informed preprocessing deliver ~15 microseconds per spectrum on a single CPU, enabling gradient-based samplers like HMC/NUTS via Turing.jl to converge in minutes on a laptop. Validated against PT Challenge and BOSS data, the approach preserves accuracy and opens doors for cross-disciplinary applications in weather, climate, and materials.

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