Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification

Geraci, Gianluca; Eldred, Michael S.

doi:10.2172/1475254

Title: Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification

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Abstract

In this work we propose an approach for accelerating Uncertainty Quantification (UQ) analysis in the context of Multifidelity applications. In the presence of complex multiphysics applications, which often require a prohibitive computational cost for each evaluation, multifidelity UQ techniques try to accelerate the convergence of statistics by leveraging the in- formation collected from a larger number of a lower fidelity model realizations. However, at the-state-of-the-art, the performance of virtually all the multifidelity UQ techniques is related to the correlation between the high and low-fidelity models. In this work we proposed to design a multifidelity UQ framework based on the identification of independent important directions for each model. The main idea is that if the responses of each model can be represented in a common space, this latter can be shared to enhance the correlation when the samples are drawn with respect to it instead of the original variables. There are also two main additional advantages that follow from this approach. First, the models might be correlated even if their original parametrizations are chosen independently. Second, if the shared space between models has a lower dimensionality than the original spaces, the UQ analysis might benefit from a dimension reduction standpoint. Inmore »« less

Authors:

Geraci, Gianluca ^[1]; Eldred, Michael S. ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Sat Sep 01 00:00:00 EDT 2018

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:: 1475254

Report Number(s):: SAND2018-10817; LDRD-211665
668336

DOE Contract Number:: AC04-94AL85000; NA0003525

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; 42 ENGINEERING

Citation Formats


                    Geraci, Gianluca, and Eldred, Michael S. Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification.  United States: N. p., 2018. 
        Web.  doi:10.2172/1475254.

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                    Geraci, Gianluca, & Eldred, Michael S. Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification.  United States.  https://doi.org/10.2172/1475254

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                    Geraci, Gianluca, and Eldred, Michael S. 2018.  
        "Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification".  United States.  https://doi.org/10.2172/1475254.  https://www.osti.gov/servlets/purl/1475254.

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@article{osti_1475254,

  title        = {Leveraging Intrinsic Principal Directions for Multifidelity Uncertainty Quantification},

  author       = {Geraci, Gianluca and Eldred, Michael S.},

  abstractNote = {In this work we propose an approach for accelerating Uncertainty Quantification (UQ) analysis in the context of Multifidelity applications. In the presence of complex multiphysics applications, which often require a prohibitive computational cost for each evaluation, multifidelity UQ techniques try to accelerate the convergence of statistics by leveraging the in- formation collected from a larger number of a lower fidelity model realizations. However, at the-state-of-the-art, the performance of virtually all the multifidelity UQ techniques is related to the correlation between the high and low-fidelity models. In this work we proposed to design a multifidelity UQ framework based on the identification of independent important directions for each model. The main idea is that if the responses of each model can be represented in a common space, this latter can be shared to enhance the correlation when the samples are drawn with respect to it instead of the original variables. There are also two main additional advantages that follow from this approach. First, the models might be correlated even if their original parametrizations are chosen independently. Second, if the shared space between models has a lower dimensionality than the original spaces, the UQ analysis might benefit from a dimension reduction standpoint. In this work we designed this general framework and we also tested it on several test problems ranging from analytical functions for verification purpose, up to more challenging application problems as an aero-thermo-structural analysis and a scramjet flow analysis.},

  doi          = {10.2172/1475254},

  url          = {https://www.osti.gov/biblio/1475254},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sat Sep 01 00:00:00 EDT 2018},

  month        = {Sat Sep 01 00:00:00 EDT 2018}

}

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