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Tractable Non-Gaussian Representations in Dynamic Data Driven Coherent Fluid Map**

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Abstract

This chapter discusses the elements of a Dynamic Data Driven Applications System in the context of map** coherent environmental fluids using autonomous small unmanned aircraft. The application and and its underlying system dynamics and optimization are presented along with three key ideas. The first is that of a dynamically deformable reduced model, which enables efficacious prediction by solving non-Gaussian problems associated with coherent fluids. The second is the use of ensemble learning in nonlinear estimation, which mitigates model errors in the form of bias, reduces sampling burdens in estimation whilst offering direct state space adjustments for filtering and smoothing and producing compact posterior ensembles. The third idea is the use of tractable variational information theoretic inference in estimation that also requires minimal resampling and allows for gradient-based inferences for non-Gaussian high-dimensional problems with few samples.

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Notes

  1. 1.

    Maximum a posteriori (MAP) problem can also be solved.

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Acknowledgements

This work was in part supported by AFOSR(FA9550-12-1-0313) and NSF DBI-1146747, the MISTI seed fund, a Seaver award and an ESI seed grant. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF or AFOSR or MISTI. Special thanks to K. Emanuel, J. How, H.-L. Choi, J. Salas, P. Tagade, C. Denamiel, H. Seybold, R. Westlund, O. Gonzalez, B. Rosas and many undergraduate students and collaborators of ESSG.

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Authors and Affiliations

  1. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

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  1. Sai Ravela
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Correspondence to Sai Ravela .

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Editors and Affiliations

  1. Air Force Office of Scientific Research, Arlington, VA, USA

    Erik P. Blasch

  2. InfoSymbiotics Systems Society, Boston, MA, USA

    Frederica Darema

  3. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  4. Air Force Research Lab, Rome, NY, USA

    Alex J. Aved

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Ravela, S. (2022). Tractable Non-Gaussian Representations in Dynamic Data Driven Coherent Fluid Map**. In: Blasch, E.P., Darema, F., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-74568-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-74568-4_2

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  • Online ISBN: 978-3-030-74568-4

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