Uncertainty Quantification and Bayesian Inversion

Encyclopedia of Computational Mechanics Second Edition ◽

10.1002/9781119176817.ecm2071 ◽

2017 ◽

pp. 1-51 ◽

Author(s):

Hermann G. Matthies

Keyword(s):

Uncertainty Quantification ◽

Bayesian Inversion

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FAST BAYESIAN INVERSION METHOD FOR THE GENERALIZED PETROPHYSICAL AND COMPOSITIONAL INTERPRETATION OF MULTIPLE WELL LOGS WITH UNCERTAINTY QUANTIFICATION

10.30632/t60als-2019_ffff ◽

2019 ◽

Author(s):

Tianqi Deng ◽

Joaquin Ambia ◽

Carlos Torres-Verdín

Keyword(s):

Uncertainty Quantification ◽

Well Logs ◽

Bayesian Inversion ◽

Inversion Method ◽

Compositional Interpretation

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FIELD-SCALE GROUND-PENETRATING-RADAR TOMOGRAPHY AND UNCERTAINTY QUANTIFICATION THROUGH ENTROPY-BASED BAYESIAN INVERSION

10.4133/sageep2013-030.1 ◽

2013 ◽

Author(s):

Neil Terry ◽

Zhangshuan Hou ◽

Susan S. Hubbard

Keyword(s):

Uncertainty Quantification ◽

Ground Penetrating Radar ◽

Bayesian Inversion ◽

Field Scale ◽

Ground Penetrating

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Joint Bayesian inversion for reservoir characterization and uncertainty quantification

10.1190/1.3059194 ◽

2008 ◽

Author(s):

Tiancong Hong ◽

Mrinal K. Sen

Keyword(s):

Uncertainty Quantification ◽

Reservoir Characterization ◽

Bayesian Inversion

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Improving the reliability of POD curves in NDI methods using a Bayesian inversion approach for uncertainty quantification

10.1063/1.4940653 ◽

2016 ◽

Author(s):

A. Ben Abdessalem ◽

F. Jenson ◽

P. Calmon

Keyword(s):

Uncertainty Quantification ◽

Bayesian Inversion

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Uncertainty Quantification for Mature Field Combining the Bayesian Inversion Formalism and Experimental Design Approach

10.3997/2214-4609-pdb.9.a030 ◽

2004 ◽

Author(s):

M. Feraille ◽

F. Roggero

Keyword(s):

Experimental Design ◽

Uncertainty Quantification ◽

Design Approach ◽

Bayesian Inversion ◽

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Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2021.107954 ◽

2022 ◽

Vol 162 ◽

pp. 107954

Author(s):

Lechang Yang ◽

Sifeng Bi ◽

Matthias G.R. Faes ◽

Matteo Broggi ◽

Michael Beer

Keyword(s):

Uncertainty Quantification ◽

Probabilistic Models ◽

Bayesian Inversion

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Parameter Estimation and Uncertainty Quantification in Water Resources Modeling

10.3389/978-2-88963-674-7 ◽

2020 ◽

Keyword(s):

Parameter Estimation ◽

Water Resources ◽

Uncertainty Quantification

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UNCERTAINTY QUANTIFICATION TOWARDS FILTERING OPTIMIZATION IN SCENE MATCHING AIDED NAVIGATION SYSTEMS

International Journal for Uncertainty Quantification ◽

10.1615/int.j.uncertaintyquantification.2016016561 ◽

2016 ◽

Vol 6 (2) ◽

pp. 127-140

Author(s):

Shengdi Zhang ◽

Xiaojun Duan ◽

Lijun Peng

Keyword(s):

Uncertainty Quantification ◽

Navigation Systems ◽

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An efficient computational method for parameter identification in the context of random set theory via Bayesian inversion

International Journal for Uncertainty Quantification ◽

10.1615/int.j.uncertaintyquantification.2020031869 ◽

2020 ◽

Author(s):

Truong Vinh Hoang ◽

Hermann G. Matthies

Keyword(s):

Parameter Identification ◽

Computational Method ◽

Bayesian Inversion ◽

Random Set Theory

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INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

International Journal for Uncertainty Quantification ◽

10.1615/int.j.uncertaintyquantification.2020033186 ◽

2020 ◽

Vol 10 (4) ◽

pp. 333-349

Author(s):

Kevin de Vries ◽

Anna Nikishova ◽

Benjamin Czaja ◽

Gábor Závodszky ◽

Alfons G. Hoekstra

Keyword(s):

Gaussian Process ◽

Uncertainty Quantification ◽

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