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2015 | OriginalPaper | Chapter

Statistical Inference for Coherent Fluids

Author : Sai Ravela

Published in: Dynamic Data-Driven Environmental Systems Science

Publisher: Springer International Publishing

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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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previous chapter Objective Detection of Lagrangian Vortices in Unsteady Velocity Data

next chapter Reduced Order Probabilistic Prediction of Rogue Waves in One-Dimensional Envelope Equations

Gridded spatial fields are interchanged as vectors by rasterizing.

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Title: Statistical Inference for Coherent Fluids
Author: Sai Ravela
Publisher: Springer International Publishing
Book: Dynamic Data-Driven Environmental Systems Science
Print ISBN: 978-3-319-25137-0

Electronic ISBN: 978-3-319-25138-7

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-3-319-25138-7_12

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