A new particle filter framework uses adaptive trust measures derived from kernel bandwidths to combine theory-driven and data-driven models, achieving convergent high-dimensional data assimilation in the undersampled regime.
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Learning to Trust AI and Data-driven models in Data Assimilation through a Multifidelity Ensemble Gaussian Mixture Filter Framework
A new particle filter framework uses adaptive trust measures derived from kernel bandwidths to combine theory-driven and data-driven models, achieving convergent high-dimensional data assimilation in the undersampled regime.