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A random map implementation of implicit filters

机译:隐式过滤器的随机映射实现

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摘要

Implicit particle filters for data assimilation generate high-probability samples by representing each particle location as a separate function of a common reference variable. This representation requires that a certain underdetermined equation be solved for each particle and at each time an observation becomes available. We present a new implementation of implicit filters in which we find the solution of the equation via a random map. As examples, we assimilate data for a stochastically driven Lorenz system with sparse observations and for a stochastic Kuramoto-Sivashinsky equation with observations that are sparse in both space and time.
机译:用于数据同化的隐式粒子滤波器通过将每个粒子位置表示为公共参考变量的单独函数来生成高概率样本。这种表示需要为每个粒子求解某个不确定的方程式,并且每次观察都可用。我们提出了一种隐式滤波器的新实现,其中我们通过随机映射找到方程的解。例如,我们将随机驱动的Lorenz系统的数据同化为稀疏,而随机Kuramoto-Sivashinsky方程的数据同化为时空稀疏。

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