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Statistical Inference for Coherent Fluids

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Dynamic Data-Driven Environmental Systems Science (DyDESS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8964))

Abstract

A non-parametric perceptual organization for coherent fluids is proposed, motivated by the observation that ignoring coherence can be disastrous for inference. Detecting coherence features and establishing correspondence can be challenging for sparse measurements and complex structures in fluid fields. Therefore, a non-parametric representation using deformation (geometry) and amplitude (appearance) is developed. It is first applied to Data Assimilation and Ensemble analysis problems for coherent fluids, following which new methods for Principal Modes, Random Fields, Variational Blending and Reduced Order Modeling are introduced. Simple examples illustrating application suggest broad utility in environmental inference, verification, representation and modeling.

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Notes

  1. 1.

    Gridded spatial fields are interchanged as vectors by rasterizing.

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Author information

Authors and Affiliations

  1. Earth Signals and Systems Group, Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, USA

    Sai Ravela

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  1. Sai Ravela
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Correspondence to Sai Ravela .

Editor information

Editors and Affiliations

  1. Massachusetts Inst. of Technology, Cambridge, Massachusetts, USA

    Sai Ravela

  2. VA Polytechnic Institute and State Univ., Blacksburg, Virginia, USA

    Adrian Sandu

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Ravela, S. (2015). Statistical Inference for Coherent Fluids. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-25138-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25137-0

  • Online ISBN: 978-3-319-25138-7

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