Detection of optimal models in parameter space with support vector machines

Vasily Demyanov, Alexei Pozdnoukhov, Michael Andrew Christie, Mikhail Kanevski

    Research output: Chapter in Book/Report/Conference proceedingChapter


    The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values’ combination.
    Original languageEnglish
    Title of host publication geoENV VII – geostatistics for environmental applications
    Subtitle of host publicationproceedings of the seventh European Conference on Geostatistics for Environmental Applications
    EditorsPeter M. Atkinson, Christopher D. Lloyd
    Place of PublicationDordrecht
    Number of pages14
    ISBN (Electronic)9789048123223
    ISBN (Print)9789048123216
    Publication statusPublished - May 2010

    Publication series

    NameQuantitative geology and geostatistics
    ISSN (Print)0924-1973


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