论文标题
对参数估计的ENKF的评论
A Review of the EnKF for Parameter Estimation
论文作者
论文摘要
合奏Kalman过滤器是一种知名且著名的数据同化算法。它通过样本平均值和协方差矩阵来更新颗粒的集合，特别是用于高维问题的重要性。在本章中，我们提出了一个相对较新的主题，即ENKF在反问题上的应用，称为集合卡尔曼倒置（EKI）。 EKI用于参数估计，可以将其视为用于PDE构成的逆问题的黑盒优化器。我们在本章中介绍了讨论方法的回顾，同时介绍了新兴领域和新的研究领域，其中在地球科学和数值天气预测中引起的许多有趣模型中提供了数值实验。
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.
