A generative transfer framework using iterative path-wise tilting integrated with conditional flow matching recovers target entropic optimal transport couplings from reference samples, achieving O(δ) convergence in Wasserstein-1 distance.
Journal of Geophysical Research: Oceans , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
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Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
An MLP predicts the covariance difference between limited and large ensembles and corrects the EnKF forecast covariance via element-wise scaling, yielding higher accuracy than standard EnKF on Lorenz-63 and Lorenz-96.
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Machine Learning-Based Covariance Correction for Ensemble Kalman Filter with Limited Ensemble Size
An MLP predicts the covariance difference between limited and large ensembles and corrects the EnKF forecast covariance via element-wise scaling, yielding higher accuracy than standard EnKF on Lorenz-63 and Lorenz-96.