Sequential model identification with reversible jump ensemble data assimilation method

IF 1.6 2区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Yue Huan, Hai Xiang Lin
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Abstract

In data assimilation (DA) schemes, the form representing the processes in the evolution models are pre-determined except some parameters to be estimated. In some applications, such as the contaminant solute transport model and the gas reservoir model, the modes in the equations within the evolution model cannot be predetermined from the outset and may change with the time. We propose a framework of sequential DA method named Reversible Jump Ensemble Filter (RJEnF) to identify the governing modes of the evolution model over time. The main idea is to introduce the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method to the DA schemes to fit the situation where the modes of the evolution model are unknown and the dimension of the parameters is changing. Our framework allows us to identify the modes in the evolution model and their changes, as well as estimate the parameters and states of the dynamic system. Numerical experiments are conducted and the results show that our framework can effectively identify the underlying evolution models and increase the predictive accuracy of DA methods.

Abstract Image

采用可逆跃迁集合数据同化方法进行序列模型识别
在数据同化(DA)方案中,除了一些需要估算的参数外,演化模型中表示过程的形式都是预先确定的。在某些应用中,如污染物溶质传输模型和储气库模型,演化模型中的方程模式无法从一开始就预先确定,可能会随着时间的推移而改变。我们提出了一种名为 "可逆跃迁集合滤波器(RJEnF)"的序列分析方法框架,用于识别演化模型随时间变化的支配模式。其主要思想是将可逆跃迁马尔可夫链蒙特卡洛(RJMCMC)方法引入数模转换方案,以适应演化模型模式未知且参数维度不断变化的情况。我们的框架允许我们识别演化模型中的模式及其变化,以及估计动态系统的参数和状态。我们进行了数值实验,结果表明我们的框架能有效识别底层演化模型,提高数模转换方法的预测精度。
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来源期刊
Statistics and Computing
Statistics and Computing 数学-计算机:理论方法
CiteScore
3.20
自引率
4.50%
发文量
93
审稿时长
6-12 weeks
期刊介绍: Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.
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