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Y. Tarumi

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#machine learning Preprint Sep 2026

PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

Many physical data assimilation (DA) workflows require smoothing methods that represent non-Gaussian posteriors over physical state variables, scale to high-dimensional simulators, train from observation windows alone, and remain compatible with calibration of the prescribed simulator. We introduce PR-Smoother, a simul...

Y. Tarumi · 0 citations