QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
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A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.
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A Data-Consistent Approach to Ensemble Filtering
QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
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Reinforcement Learning, Optimal Control, and Bayesian Filtering in Data Assimilation
A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
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Graph neural network for colliding particles with an application to sea ice floe modeling
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.