skip to content
Dynamic Active Subspaces: A Data-driven Approach to Computing Time-dependent Active Subspaces in Dynamical Systems Preview this item
ClosePreview this item
Checking...

Dynamic Active Subspaces: A Data-driven Approach to Computing Time-dependent Active Subspaces in Dynamical Systems

Author: Izabel Pirimai Aguiar; Paul G Constantine
Publisher: Ann Arbor : ProQuest Dissertations & Theses, 2018.
Dissertation: M.S. University of Colorado at Boulder 2018.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Publication:Masters Abstracts International, 58-01(E)
Summary:
Computational models are aiding in the advancement of science -- from biological, to engineering, to social systems. To trust the predictions of computational models, however, we must understand how the errors in the models' inputs (i.e., through measurement error) affect the output of the systems: we must quantify the uncertainty that results from these input errors. Uncertainty quantification (UQ) becomes  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Genre/Form: Theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Izabel Pirimai Aguiar; Paul G Constantine
ISBN: 9780438442801 0438442806
OCLC Number: 1081369060
Language Note: English.
Notes: Source: Masters Abstracts International, Volume: 58-01M(E).
Advisors: Paul G. Constantine Committee members: Elizabeth Bradley; James Curry; Gianluca Iaccarino.
Description: 1 electronic resource (59 pages)
Responsibility: Izabel Pirimai Aguiar.

Abstract:

Computational models are aiding in the advancement of science -- from biological, to engineering, to social systems. To trust the predictions of computational models, however, we must understand how the errors in the models' inputs (i.e., through measurement error) affect the output of the systems: we must quantify the uncertainty that results from these input errors. Uncertainty quantification (UQ) becomes computationally complex when there are many parameters in the model. In such cases it is useful to reduce the dimension of the problem by identifying unimportant parameters and disregarding them for UQ studies. This makes an otherwise intractable UQ problem tractable. Active subspaces extend this idea by identifying important linear combinations of parameters, enabling more powerful and effective dimension reduction. Although active subspaces give model insight and computational tractability for scalar-valued functions, it is not enough. This analysis does not extend to time-dependent systems. In this thesis we discuss time-dependent, dynamic active subspaces. We develop a methodology by which to compute and approximate dynamic active subspaces, and introduce the analytical form of dynamic active subspaces for two cases. To highlight these methods we find dynamic active subspaces for a linear harmonic oscillator and a nonlinear enzyme kinetics system.

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


Primary Entity

<http://www.worldcat.org/oclc/1081369060> # Dynamic Active Subspaces: A Data-driven Approach to Computing Time-dependent Active Subspaces in Dynamical Systems
    a schema:Book, bgn:Thesis, schema:MediaObject, schema:CreativeWork, pto:Web_document ;
    bgn:inSupportOf "" ;
    library:oclcnum "1081369060" ;
    library:placeOfPublication <http://id.loc.gov/vocabulary/countries/miu> ;
    schema:author <http://experiment.worldcat.org/entity/work/data/8856947842#Person/aguiar_izabel_pirimai> ; # Izabel Pirimai Aguiar
    schema:contributor <http://experiment.worldcat.org/entity/work/data/8856947842#Person/constantine_paul_g> ; # Paul G. Constantine
    schema:datePublished "2018" ;
    schema:description "Computational models are aiding in the advancement of science -- from biological, to engineering, to social systems. To trust the predictions of computational models, however, we must understand how the errors in the models' inputs (i.e., through measurement error) affect the output of the systems: we must quantify the uncertainty that results from these input errors. Uncertainty quantification (UQ) becomes computationally complex when there are many parameters in the model. In such cases it is useful to reduce the dimension of the problem by identifying unimportant parameters and disregarding them for UQ studies. This makes an otherwise intractable UQ problem tractable. Active subspaces extend this idea by identifying important linear combinations of parameters, enabling more powerful and effective dimension reduction. Although active subspaces give model insight and computational tractability for scalar-valued functions, it is not enough. This analysis does not extend to time-dependent systems. In this thesis we discuss time-dependent, dynamic active subspaces. We develop a methodology by which to compute and approximate dynamic active subspaces, and introduce the analytical form of dynamic active subspaces for two cases. To highlight these methods we find dynamic active subspaces for a linear harmonic oscillator and a nonlinear enzyme kinetics system."@en ;
    schema:exampleOfWork <http://worldcat.org/entity/work/id/8856947842> ;
    schema:genre "Theses"@en ;
    schema:inLanguage "en" ;
    schema:name "Dynamic Active Subspaces: A Data-driven Approach to Computing Time-dependent Active Subspaces in Dynamical Systems"@en ;
    schema:productID "1081369060" ;
    schema:url <https://colorado.idm.oclc.org/login?url=http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10826096> ;
    schema:workExample <http://worldcat.org/isbn/9780438442801> ;
    wdrs:describedby <http://www.worldcat.org/title/-/oclc/1081369060> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/8856947842#Person/aguiar_izabel_pirimai> # Izabel Pirimai Aguiar
    a schema:Person ;
    schema:familyName "Aguiar" ;
    schema:givenName "Izabel Pirimai" ;
    schema:name "Izabel Pirimai Aguiar" ;
    .

<http://experiment.worldcat.org/entity/work/data/8856947842#Person/constantine_paul_g> # Paul G. Constantine
    a schema:Person ;
    schema:familyName "Constantine" ;
    schema:givenName "Paul G." ;
    schema:name "Paul G. Constantine" ;
    .

<http://worldcat.org/isbn/9780438442801>
    a schema:ProductModel ;
    schema:isbn "0438442806" ;
    schema:isbn "9780438442801" ;
    .

<http://www.worldcat.org/title/-/oclc/1081369060>
    a genont:InformationResource, genont:ContentTypeGenericResource ;
    schema:about <http://www.worldcat.org/oclc/1081369060> ; # Dynamic Active Subspaces: A Data-driven Approach to Computing Time-dependent Active Subspaces in Dynamical Systems
    schema:dateModified "2019-02-09" ;
    void:inDataset <http://purl.oclc.org/dataset/WorldCat> ;
    .


Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.