A Review of the EnKF for Parameter Estimation
The ensemble Kalman filter is a well-known and celebrated data assimilation algorithm. It is of particular relevance as it used for high-dimensional problems, by updating an ensemble of particles through a sample mean and covariance matrices. In this chapter we present a relatively recent topic whic...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
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IntechOpen
2022
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| Acceso en línea: | https://www.intechopen.com/chapters/84583 |
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| Sumario: | The ensemble Kalman filter is a well-known and celebrated data assimilation algorithm. It is of particular relevance as it used for high-dimensional problems, by updating an ensemble of particles through a sample mean and covariance matrices. In this chapter we present a relatively recent topic which is the application of the EnKF to inverse problems, known as ensemble Kalman Inversion (EKI). EKI is used for parameter estimation, which can be viewed as a black-box optimizer for PDE-constrained inverse problems. We present in this chapter a review of the discussed methodology, while presenting emerging and new areas of research, where numerical experiments are provided on numerous interesting models arising in geosciences and numerical weather prediction. |
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| ISBN: | 978-1-80355-222-4 |
| DOI: | 10.5772/intechopen.108218 |