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Journal articles
Surrogate modeling based on resampled polynomial chaos expansions
Abstract : In surrogate modeling, polynomial chaos expansion (PCE) is popularly utilized to represent the random
model responses, which are computationally expensive and usually obtained by deterministic numerical
modeling approaches including fi nite-element and finite-di fference time-domain methods. Recently, e orts
have been made on improving the prediction performance of the PCE-based model and building efficiency
by only selecting the influential basis polynomials (e.g., via the approach of least angle regression). This
paper proposes an approach, named as resampled PCE (rPCE), to further optimize the selection by making
use of the knowledge that the true model is fixed despite the statistical uncertainty inherent to sampling
in the training. By simulating data variation via resampling (k-fold division utilized here) and collecting
the selected polynomials with respect to all resamples, polynomials are ranked mainly according to the
selection frequency. The resampling scheme (the value of k here) matters much and various con figurations
are considered and compared. The proposed resampled PCE is implemented with two popular selection
techniques, namely least angle regression and orthogonal matching pursuit, and a combination thereof. The
performance of the proposed algorithm is demonstrated on two analytical examples, a benchmark problem
in structural mechanics, as well as a realistic case study in computational dosimetry.
https://hal-centralesupelec.archives-ouvertes.fr/hal-01889651
Contributor : Dominique Lesselier <>
Submitted on : Sunday, October 7, 2018 - 3:04:24 PM
Last modification on : Thursday, December 10, 2020 - 4:47:14 PM
Contributor : Dominique Lesselier <>
Submitted on : Sunday, October 7, 2018 - 3:04:24 PM
Last modification on : Thursday, December 10, 2020 - 4:47:14 PM
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