EnSF-LR combines nonlinear score-based analysis on observed components with EnKF-style linear regression on unobserved components via ensemble covariance, achieving lower full-state RMSE than EnSF and EnKF in nonlinear-observation tests on Lorenz-63 and Lorenz-96.
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EOMC shows that chaotic systems like Lorenz-96 and tokamak turbulence are best captured as metastable switches between persistent low-dimensional manifolds with slowly decreasing exit times.
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A Two-Step Ensemble Score Filter for Data Assimilation in Partially Observed Systems
EnSF-LR combines nonlinear score-based analysis on observed components with EnKF-style linear regression on unobserved components via ensemble covariance, achieving lower full-state RMSE than EnSF and EnKF in nonlinear-observation tests on Lorenz-63 and Lorenz-96.
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Linearly-scalable and entropy-optimal learning of nonstationary and nonlinear manifolds
EOMC shows that chaotic systems like Lorenz-96 and tokamak turbulence are best captured as metastable switches between persistent low-dimensional manifolds with slowly decreasing exit times.