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arXiv · 1807.08351

Data Assimilation: The Schr\"odinger Perspective

Sebastian Reich
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Abstract

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 Schr\"odinger's boundary value problem for stochastic processes in particular.

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Sebastian Reich. 2018-07-22. Data Assimilation: The Schr\"odinger Perspective. https://doi.org/10.1017/s0962492919000011

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