Surrogate-based parameter inference in debris flow model.
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| Title: | Surrogate-based parameter inference in debris flow model. |
|---|---|
| Authors: | Navarro, Maria1 marianj61@gmail.com, Le Maître, Olivier P.2 olm@limsi.fr, Hoteit, Ibrahim3 ibrahim.hoteit@kaust.edu.sa, George, David L.4 dgeorge@usgs.gov, Mandli, Kyle T.5 kyle.mandli@columbia.edu, Knio, Omar M.1 omar.knio@kaust.edu.sa |
| Source: | Computational Geosciences. Dec2018, Vol. 22 Issue 6, p1447-1463. 17p. |
| Subjects: | Bayesian analysis, Polynomial chaos, Dynamical systems, Data analysis, X-ray diffraction |
| Abstract: | This work tackles the problem of calibrating the unknown parameters of a debris flow model with the drawback that the information regarding the experimental data treatment and processing is not available. In particular, we focus on the evolution over time of the flow thickness of the debris with dam-break initial conditions. The proposed methodology consists of establishing an approximation of the numerical model using a polynomial chaos expansion that is used in place of the original model, saving computational burden. The values of the parameters are then inferred through a Bayesian approach with a particular focus on inference discrepancies that some of the important features predicted by the model exhibit. We build the model approximation using a preconditioned non-intrusive method and show that a suitable prior parameter distribution is critical to the construction of an accurate surrogate model. The results of the Bayesian inference suggest that utilizing directly the available experimental data could lead to incorrect conclusions, including the over-determination of parameters. To avoid such drawbacks, we propose to base the inference on few significant features extracted from the original data. Our experiments confirm the validity of this approach, and show that it does not lead to significant loss of information. It is further computationally more efficient than the direct approach, and can avoid the construction of an elaborate error model. [ABSTRACT FROM AUTHOR] |
| Copyright of Computational Geosciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 133019625 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Surrogate-based parameter inference in debris flow model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Navarro%2C+Maria%22">Navarro, Maria</searchLink><relatesTo>1</relatesTo><i> marianj61@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Le+Maître%2C+Olivier+P%2E%22">Le Maître, Olivier P.</searchLink><relatesTo>2</relatesTo><i> olm@limsi.fr</i><br /><searchLink fieldCode="AR" term="%22Hoteit%2C+Ibrahim%22">Hoteit, Ibrahim</searchLink><relatesTo>3</relatesTo><i> ibrahim.hoteit@kaust.edu.sa</i><br /><searchLink fieldCode="AR" term="%22George%2C+David+L%2E%22">George, David L.</searchLink><relatesTo>4</relatesTo><i> dgeorge@usgs.gov</i><br /><searchLink fieldCode="AR" term="%22Mandli%2C+Kyle+T%2E%22">Mandli, Kyle T.</searchLink><relatesTo>5</relatesTo><i> kyle.mandli@columbia.edu</i><br /><searchLink fieldCode="AR" term="%22Knio%2C+Omar+M%2E%22">Knio, Omar M.</searchLink><relatesTo>1</relatesTo><i> omar.knio@kaust.edu.sa</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computational+Geosciences%22">Computational Geosciences</searchLink>. Dec2018, Vol. 22 Issue 6, p1447-1463. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Polynomial+chaos%22">Polynomial chaos</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22X-ray+diffraction%22">X-ray diffraction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This work tackles the problem of calibrating the unknown parameters of a debris flow model with the drawback that the information regarding the experimental data treatment and processing is not available. In particular, we focus on the evolution over time of the flow thickness of the debris with dam-break initial conditions. The proposed methodology consists of establishing an approximation of the numerical model using a polynomial chaos expansion that is used in place of the original model, saving computational burden. The values of the parameters are then inferred through a Bayesian approach with a particular focus on inference discrepancies that some of the important features predicted by the model exhibit. We build the model approximation using a preconditioned non-intrusive method and show that a suitable prior parameter distribution is critical to the construction of an accurate surrogate model. The results of the Bayesian inference suggest that utilizing directly the available experimental data could lead to incorrect conclusions, including the over-determination of parameters. To avoid such drawbacks, we propose to base the inference on few significant features extracted from the original data. Our experiments confirm the validity of this approach, and show that it does not lead to significant loss of information. It is further computationally more efficient than the direct approach, and can avoid the construction of an elaborate error model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computational Geosciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10596-018-9765-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1447 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Polynomial chaos Type: general – SubjectFull: Dynamical systems Type: general – SubjectFull: Data analysis Type: general – SubjectFull: X-ray diffraction Type: general Titles: – TitleFull: Surrogate-based parameter inference in debris flow model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Navarro, Maria – PersonEntity: Name: NameFull: Le Maître, Olivier P. – PersonEntity: Name: NameFull: Hoteit, Ibrahim – PersonEntity: Name: NameFull: George, David L. – PersonEntity: Name: NameFull: Mandli, Kyle T. – PersonEntity: Name: NameFull: Knio, Omar M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 14200597 Numbering: – Type: volume Value: 22 – Type: issue Value: 6 Titles: – TitleFull: Computational Geosciences Type: main |
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