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Title: Polynomial Surrogate Construction for Computational Models.

Abstract

Abstract not provided.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1373064
Report Number(s):
SAND2016-7019D
645985
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Intern Symposium.
Country of Publication:
United States
Language:
English

Citation Formats

Teichman, Sarah, and Sargsyan, Khachik. Polynomial Surrogate Construction for Computational Models.. United States: N. p., 2016. Web.
Teichman, Sarah, & Sargsyan, Khachik. Polynomial Surrogate Construction for Computational Models.. United States.
Teichman, Sarah, and Sargsyan, Khachik. Fri . "Polynomial Surrogate Construction for Computational Models.". United States. doi:. https://www.osti.gov/servlets/purl/1373064.
@article{osti_1373064,
title = {Polynomial Surrogate Construction for Computational Models.},
author = {Teichman, Sarah and Sargsyan, Khachik},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jul 01 00:00:00 EDT 2016},
month = {Fri Jul 01 00:00:00 EDT 2016}
}

Conference:
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  • Costly and/or insufficiently robust simulations or experiments can often pose difficulties when their use extends well beyond a single evaluation. This is case with the numerous evaluations of uncertainty quantification, when an algebraic model is needed for optimization, as well as numerous other areas. To overcome these difficulties, we generate an accurate set of algebraic surrogate models of disaggregated process blocks of the experiment or simulation. We developed a method that uses derivative-based and derivative-free optimization alongside machine learning and statistical techniques to generate the set of surrogate models using data sampled from experiments or detailed simulations. Our method beginsmore » by building a low-complexity surrogate model for each block from an initial sample set. The model is built using a best subset technique that leverages a mixed-integer linear problem formulation to allow for very large initial basis sets. The models are then tested, exploited, and improved through the use of derivative-free solvers to adaptively sample new simulation or experimental points. The sets of surrogate models from each disaggregated process block are then combined with heat and mass balances around each disaggregated block to generate a full algebraic model of the process. The full model can be used for cheap and accurate evaluations of the original simulation or experiment or combined with design specifications and an objective for nonlinear optimization.« less
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  • In numerical dosimetry, the recent advances in high performance computing led to a strong reduction of the required computational time to assess the specific absorption rate (SAR) characterizing the human exposure to electromagnetic waves. However, this procedure remains time-consuming and a single simulation can request several hours. As a consequence, the influence of uncertain input parameters on the SAR cannot be analyzed using crude Monte Carlo simulation. The solution presented here to perform such an analysis is surrogate modeling. This paper proposes a novel approach to build such a surrogate model from a design of experiments. Considering a sparse representationmore » of the polynomial chaos expansions using least-angle regression as a selection algorithm to retain the most influential polynomials, this paper proposes to use the selected polynomials as regression functions for the universal Kriging model. The leave-one-out cross validation is used to select the optimal number of polynomials in the deterministic part of the Kriging model. The proposed approach, called LARS-Kriging-PC modeling, is applied to three benchmark examples and then to a full-scale metamodeling problem involving the exposure of a numerical fetus model to a femtocell device. The performances of the LARS-Kriging-PC are compared to an ordinary Kriging model and to a classical sparse polynomial chaos expansion. The LARS-Kriging-PC appears to have better performances than the two other approaches. A significant accuracy improvement is observed compared to the ordinary Kriging or to the sparse polynomial chaos depending on the studied case. This approach seems to be an optimal solution between the two other classical approaches. A global sensitivity analysis is finally performed on the LARS-Kriging-PC model of the fetus exposure problem.« less