Multiway Ensemble Kalman Filter

Yu Wang; Alfred Hero

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arXiv · 2112.04322

Multiway Ensemble Kalman Filter

Yu Wang·Alfred Hero

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Abstract

In this work, we study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equations (PDEs). We consider several state-of-the-art multiway covariance and inverse covariance (precision) matrix estimators and examine their pros and cons in terms of accuracy and interpretability in the context of physics-driven forecasting when incorporated into the ensemble Kalman filter (EnKF). In particular, we show that multiway data generated from the Poisson and the convection-diffusion types of PDEs can be accurately tracked via EnKF when integrated with appropriate covariance and precision matrix estimators.

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BibTeX RIS

Yu Wang, Alfred Hero. 2021-12-08. Multiway Ensemble Kalman Filter. https://arxiv.org/abs/2112.04322

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