AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS
نویسندگان
چکیده
منابع مشابه
Analysis of the Ensemble Kalman Filter for Inverse Problems
The ensemble Kalman filter (EnKF) is a widely used methodology for state estimation in partial, noisily observed dynamical systems, and for parameter estimation in inverse problems. Despite its widespread use in the geophysical sciences, and its gradual adoption in many other areas of application, analysis of the method is in its infancy. Furthermore, much of the existing analysis deals with th...
متن کاملAn Ensemble Kalman Filter Implementation Based on 1 Modified Cholesky Decomposition for Inverse
This paper develops an efficient implementation of the ensemble Kalman filter based 5 on a modified Cholesky decomposition for inverse covariance matrix estimation. This implementation 6 is named EnKF-MC. Background errors corresponding to distant model components with respect 7 to some radius of influence are assumed to be conditionally independent. This allows to obtain 8 sparse estimators of...
متن کاملIterative Inversion Methods for Statistical Inverse Problems
In this paper we discuss general regularization estimators. This class includes Tikhonov type and spectral cut-off estimators as well as iterative methods, such as ν-methods and the Landweber iteration. The latter estimators achieve the same (optimal) convergence rates as spectral cut-off, but do not require explicit spectral information on the operator and are often much faster to compute than...
متن کاملAdaptive ensemble Kalman filtering of nonlinear systems
A necessary ingredient of an ensemble Kalman filter is covariance inflation [1], used to control filter divergence and compensate for model error. There is an ongoing search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra [2] enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the...
متن کاملAn Image-based Ensemble Kalman Filter for Motion Estimation
This paper designs an Image-based Ensemble Kalman Filter (IEnKF), whose components are defined only from image properties, to estimate motion on image sequences. The key elements of this filter are, first, the construction of the initial ensemble, and second, the propagation in time of this ensemble on the studied temporal interval. Both are analyzed in the paper and their impact on results is ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal for Uncertainty Quantification
سال: 2019
ISSN: 2152-5080
DOI: 10.1615/int.j.uncertaintyquantification.2019029059