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.
R., 1983: Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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A semi-supervised VAE trained on Skyrme EOS data reconstructs equations of state with mean absolute percentage errors under 0.14% using two supervised observables (M_max, R_1.4) and one variational latent variable.
citing papers explorer
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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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A Semi-Supervised Variational Autoencoder for Generating Neutron Star Equations of State
A semi-supervised VAE trained on Skyrme EOS data reconstructs equations of state with mean absolute percentage errors under 0.14% using two supervised observables (M_max, R_1.4) and one variational latent variable.