Piecemeal Reduction of Models of Large Networks

Francis, Benjamin L.; Transtrum, Mark K.; Sarić, Andrija T.; Stanković, Aleksandra

doi:10.1109/cdc45484.2021.9683471

2021 60th IEEE Conference on Decision and Control (CDC) 2021

DOI: 10.1109/cdc45484.2021.9683471

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Piecemeal Reduction of Models of Large Networks

Benjamin L. Francis

Mark K. Transtrum

Andrija T. Sarić

et al.

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2024

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Information Geometry in Underwater Acoustics: Tutorial, Case Study, and Outlook

Spendlove,

Mortenson,

Neilsen

et al. 2024

J. Theor. Comp. Acout.

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This tutorial demonstrates the use of information geometry tools in analyzing environmental parameter sensitivities in underwater acoustics. Sensitivity analyses quantify how well data can constrain model parameters, with application to inverse problems like geoacoustic inversion. A review of examples of parameter sensitivity methods and their application to problems in underwater acoustics is given, roughly grouped into “local” and “non-local” methods. Local methods such as Fisher information and Cramér-Rao bounds have important connections to information geometry. Information Geometry combines the fields of information theory and differential geometry by interpreting a model as a Riemannian manifold, known as the model manifold, that encodes both local and global parameter sensitivities. As an example, 2-dimensional model manifold slices are constructed for the Pekeris waveguide with sediment attenuation, for a vertical array of hydrophones. This example demonstrates how effective, reduced-order models emerge in certain parameter limits, which correspond to boundaries of the model manifold. This example also demonstrates how the global structure of the model manifold influences the local sensitivities quantified by the Fisher information matrix. This paper motivates future work to utilize information geometry methods for experimental design and model reduction applied to more complex modeling scenarios in underwater acoustics.

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Information Geometry in Underwater Acoustics: Tutorial, Case Study, and Outlook

Spendlove,

Mortenson,

Neilsen

et al. 2024

J. Theor. Comp. Acout.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Piecemeal Reduction of Models of Large Networks

Cited by 1 publication

References 28 publications

Information Geometry in Underwater Acoustics: Tutorial, Case Study, and Outlook

Information Geometry in Underwater Acoustics: Tutorial, Case Study, and Outlook

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