Comparative Study of Kriging and Support Vector Regression for Structural Engineering Applications

Abstract : Metamodeling techniques have been widely used as substitutes of high-fidelity and time-consuming models in various engineering applications. Examples include polynomial chaos expansions, neural networks, Kriging or support vector regression. This papers attempts to compare the latter two in different case studies so as to assess their relative efficiency on simulation-based analyses. Similarities are drawn between these two metamodels types leading to the use of anisotropy for SVR. Such a feature is not commonly used in the SVR related literature. A special care is given to a proper automatic calibration of the model hyperparameters by using an efficient global search algorithm, namely the covariance matrix adaptation - evolution scheme (CMA-ES). Variants of these two metamodels, associated with various kernel or auto-correlation functions, are first compared on analytical functions and then on finite-element-based models. From the comprehensive comparison, it is concluded that anisotropy in the two metamodels clearly improves their accuracy. In general, anisotropic L2-SVR with the Matérn kernels is shown to be the most effective metamodel.
Document type :
Journal articles
Liste complète des métadonnées

Cited literature [3 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01893274
Contributor : Maliki Moustapha <>
Submitted on : Thursday, October 11, 2018 - 11:36:22 AM
Last modification on : Tuesday, March 5, 2019 - 9:30:12 AM
Document(s) archivé(s) le : Saturday, January 12, 2019 - 1:54:36 PM

File

RSUQ-2018-001.pdf
Files produced by the author(s)

Identifiers

Citation

Maliki Moustapha, Jean-Marc Bourinet, Benoît Guillaume, Bruno Sudret. Comparative Study of Kriging and Support Vector Regression for Structural Engineering Applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, American Society of Civil Engineers (ASCE), 2018, 4 (2), ⟨10.1061/ajrua6.0000950⟩. ⟨hal-01893274⟩

Share

Metrics

Record views

35

Files downloads

75