Data assimilation addresses the general problem of how to combine model-based predictions with partial and noisy observations of the process in an optimal manner. This survey focuses on sequential data assimilation techniques using probabilistic particle-based algorithms. In addition to surveying recent developments for discrete- and continuous-time data assimilation, both in terms of mathematical foundations and algorithmic implementations, we also provide a unifying framework from the perspective of coupling of measures, and Schrödinger’s boundary value problem for stochastic processes in particular.

中文翻译：

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数据同化：薛定谔的观点

数据同化解决了如何以最佳方式将基于模型的预测与过程的部分和噪声观察结合起来的一般问题。这项调查的重点是使用基于概率粒子的算法的顺序数据同化技术。除了在数学基础和算法实现方面调查离散和连续时间数据同化的最新进展外，我们还从度量耦合的角度提供了一个统一的框架，特别是随机过程的薛定谔边值问题.