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Data assimilation: The Schrodinger perspective
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DOI：10.1017/S0962492919000011.png)
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Abstract
En 中文
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 Schrodinger's boundary value problem for stochastic processes in particular.
Keywords:
ENSEMBLE KALMAN FILTER
MONTE-CARLO
PARTICLE FILTERS
BUCY FILTER
DIFFUSION
STABILITY
EQUATIONS
ACCURACY
GRADIENT
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