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A machine-learning enabled framework for quantifying uncertainties in parameters of computational models.

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Title: A machine-learning enabled framework for quantifying uncertainties in parameters of computational models.
Authors: Roper, Taylor1, taylor.roper@ucdenver.edu, Hakula, Harri2, Harri.Hakula@aalto.fi, Butler, Troy1, troy.butler@ucdenver.edu
Source: Computers & Mathematics with Applications; Mar2025, Vol. 182, p184-212, 29p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 183278713
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A machine-learning enabled framework for quantifying uncertainties in parameters of computational models.
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  Data: <searchLink fieldCode="AU" term="%22Roper%2C+Taylor%22">Roper, Taylor</searchLink><relatesTo>1</relatesTo>, <i>taylor.roper@ucdenver.edu</i><br /><searchLink fieldCode="AU" term="%22Hakula%2C+Harri%22">Hakula, Harri</searchLink><relatesTo>2</relatesTo>, <i>Harri.Hakula@aalto.fi</i><br /><searchLink fieldCode="AU" term="%22Butler%2C+Troy%22">Butler, Troy</searchLink><relatesTo>1</relatesTo>, <i>troy.butler@ucdenver.edu</i>
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=183278713
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.camwa.2025.01.030
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      – Code: eng
        Text: English
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        PageCount: 29
        StartPage: 184
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      – TitleFull: A machine-learning enabled framework for quantifying uncertainties in parameters of computational models.
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              Text: Mar2025
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