Performance assessment of the maximum likelihood ensemble filter and the ensemble Kalman filters for nonlinear problems

DOI10.1007/s40687-022-00359-7zbMath1502.86010OpenAlexW4300862268MaRDI QIDQ2093059

Milija Zupanski, Xinfeng Gao, Xuemin Tu, Yi Jun Wang

Publication date: 4 November 2022

Published in: Research in the Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40687-022-00359-7

zbMATH Keywords

data assimilationensemble Kalman filterCFD modeling with data assimilationensemble data assimilation methodsmaximum likelihood ensemble filter

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Geostatistics (86A32) Computational methods for problems pertaining to geophysics (86-08)

Uses Software

• EnKF

Cites Work

• Unnamed Item
• Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
• A steepest-descent method for optimization of mechanical systems
• Stochastic processes and filtering theory
• Comparison of sequential data assimilation methods for the Kuramoto-Sivashinsky equation
• Numerical Optimization
• The Iterated Kalman Smoother as a Gauss–Newton Method
• The maximum likelihood ensemble filter for computational flame and fluid dynamics
• An Iterative Ensemble Kalman Filter
• Data Assimilation

This page was built for publication: Performance assessment of the maximum likelihood ensemble filter and the ensemble Kalman filters for nonlinear problems