Multiway Ensemble Kalman Filter

• URL: http://arxiv.org/abs/2112.04322v1
• Date: Wed, 8 Dec 2021 15:04:34 GMT
• Title: Multiway Ensemble Kalman Filter
• Authors: Yu Wang and Alfred Hero
• Abstract summary: We study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equations (PDEs) We show that multiway data generated from the Poisson and the convection-diffusion types of PDEs can be accurately tracked via the ensemble Kalman filter (EnKF)
• Score: 9.0932688770957
• License: http://creativecommons.org/licenses/by/4.0/
• 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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