• Login
    View Item 
    •   Home
    • Research
    • Technical Reports
    • View Item
    •   Home
    • Research
    • Technical Reports
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Inverse problems and uncertainty quantification

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    NonLinBU_fuer_control.pdf
    Size:
    1.559Mb
    Format:
    PDF
    Description:
    We develop an approximation of the expensive Bayesian update formula
    Download
    Type
    Technical Report
    Authors
    Litvinenko, Alexander cc
    Matthies, Hermann G.
    KAUST Department
    SRI Uncertainty Quantification Center
    Extreme Computing Research Center
    Date
    2013-12-18
    Permanent link to this record
    http://hdl.handle.net/10754/623698
    
    Metadata
    Show full item record
    Abstract
    In a Bayesian setting, inverse problems and uncertainty quantification (UQ)— the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. This is especially the case as together with a functional or spectral approach for the forward UQ there is no need for time- consuming and slowly convergent Monte Carlo sampling. The developed sampling- free non-linear Bayesian update is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisa- tion to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example of the chaotic Lorenz 84 model, where we experiment with the influence of different observation or measurement operators on the update.
    Sponsors
    KAUST, DFG
    arXiv
    1312.5048
    Additional Links
    https://arxiv.org/abs/1312.5048
    Collections
    Extreme Computing Research Center; Technical Reports

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.