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.
Kaipio and Erkki Somersalo
4 Pith papers cite this work. Polarity classification is still indexing.
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Symbolic rational-function networks recover an admissible PDE from noiseless complete measurements and select the regularization-minimizing parameterization within the architecture.
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
Bayesian inference reconstructs bathymetry from point water height measurements, improving NRMSE over adjoint optimization on real wave flume data while quantifying uncertainty.
citing papers explorer
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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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Symbolic recovery of PDEs from measurement data
Symbolic rational-function networks recover an admissible PDE from noiseless complete measurements and select the regularization-minimizing parameterization within the architecture.
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Mechanisms of Misgeneralization in Physical Sequence Modeling
Generative sequence models for physical tasks exhibit physical misgeneralization where local prediction errors propagate through physical measurements to distort aggregate distributions over quantities like distance or energy; a data deviation kernel explains and predicts the shifts and supports a内核
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Bathymetry Reconstruction by Bayesian Inference
Bayesian inference reconstructs bathymetry from point water height measurements, improving NRMSE over adjoint optimization on real wave flume data while quantifying uncertainty.