Performance assessment of the maximum likelihood ensemble filter and the ensemble Kalman filters for nonlinear problems
type: Item
instance of: scholarly article
title: Performance assessment of the maximum likelihood ensemble filter and the ensemble Kalman filters for nonlinear problems
zbMATH Open document ID: 1502.86010
author: Milija Zupanski
author: Xuemin Tu
author: Xinfeng Gao
author: Yi-Jun Wang
published in: Research in the Mathematical Sciences
publication date: 2022-11-04T00:00:00Z
Mathematics Subject Classification ID: 86A32
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 86-08
zbMATH DE Number: 7612254
zbMATH Keywords: data assimilation
zbMATH Keywords: maximum likelihood ensemble filter
zbMATH Keywords: ensemble Kalman filter
zbMATH Keywords: CFD modeling with data assimilation
zbMATH Keywords: ensemble data assimilation methods
describes a project that uses: EnKF
MaRDI profile type: Publication
full work available at URL: s40687-022-00359-7
OpenAlex ID: W4300862268
cites work: The Iterated Kalman Smoother as a Gauss–Newton Method
cites work: Data Assimilation
cites work: A steepest-descent method for optimization of mechanical systems
cites work: Stochastic processes and filtering theory
cites work: An Iterative Ensemble Kalman Filter
cites work: Q3688092
cites work: Numerical Optimization
cites work: The maximum likelihood ensemble filter for computational flame and fluid dynamics
cites work: Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
cites work: Comparison of sequential data assimilation methods for the Kuramoto-Sivashinsky equation
DOI: 10.1007/S40687-022-00359-7
description: scientific article; zbMATH DE number 7612254