A Langevinized Ensemble Kalman Filter for Large-Scale Dynamic Learning

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.5705/ss.202022.0172

Peiyi Zhang, Qifan Song, F. Liang

引用次数: 1

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用于大规模动态学习的Langevinized集成卡尔曼滤波器

集合卡尔曼滤波(EnKF)在大气科学和海洋科学的数据同化方面表现良好。然而，它不能收敛到正确的滤波分布，这使得它不能用于动态系统的不确定性量化。因此，我们在朗格万动力学的框架下重新表述了EnKF，得到了一种新的粒子滤波算法，我们称之为朗格万化EnKF (LEnKF)。LEnKF继承了EnKF的预测分析过程，并使用了随机梯度朗格万动力学(SGLD)的小批量数据。我们证明LEnKF与EnKF一样是一个顺序预处理的SGLD采样器，但它的执行速度被预测分析过程加快了。此外，随着每i阶段迭代次数的增加，LEnKF在2-Wasserstein距离方面收敛到正确的滤波分布。我们使用各种示例来演示LEnKF的性能。LEnKF不仅在状态维数和样本量方面具有可扩展性，而且对于长序列动态数据也倾向于不受样本退化的影响。中国统计:预印本doi:10.5705/ss.202022.0172

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.