What is it about?
This article introduces a novel methodology for data assimilation in dynamical systems consisting of numerous entities. By leveraging a moment kernel to decompose information from collective data typically extracted in practical applications, it significantly reduces computational costs compared to state-of-the-art methods.
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Why is it important?
The curse of dimensionality complicates the processing of statistical data in typical ensembles of dynamical systems. The methodology introduced in this article significantly reduces the dimensionality of the collected data, allowing the task to be completed with optimized computational efficiency while maintaining high performance.
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This page is a summary of: A moment-based Kalman filtering approach for estimation in ensemble systems, Chaos An Interdisciplinary Journal of Nonlinear Science, June 2024, American Institute of Physics,
DOI: 10.1063/5.0200614.
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