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A unified, open-source framework and a massive 3.3 TB dataset now enable rigorous ML-driven prediction of complex multiphase flows in porous media, accelerating research in critical energy and engineering applications.
This work introduces PosteriorBench, a benchmark for evaluating the distributional accuracy of generative inverse solvers, and reveals substantial distribution-matching gaps across current solvers, while showing that neural operators improve resolution robustness, and guidance weights and generation noise are key to posterior-variance calibration.
