Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders
Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by...
Main Authors: | Andrey A. Popov, Adrian Sandu |
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Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2022-06-01
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Series: | Frontiers in Applied Mathematics and Statistics |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2022.904687/full |
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