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Data assimilation: the Schrödinger perspective

Reich, S. (2019) Data assimilation: the Schrödinger perspective. Acta Numerica, 28. pp. 635-711. ISSN 0962-4929

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To link to this item DOI: 10.1017/S0962492919000011


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:90171
Publisher:Cambridge University Press


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