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Article Dans Une Revue Journal of Computational Physics Année : 2016

A biorthogonal decomposition for the identification and simulation of non-stationary and non-Gaussian random fields

Résumé

In this paper, a new method for the identification and simulation of non-Gaussian and non-stationary stochastic fields given a database is proposed. It is based on two successive biorthogonal decompositions aiming at representing spatio-temporal stochastic fields. The proposed double expansion allows to build the model even in the case of large-size problems by separating the time, space and random parts of the field. A Gaussian kernel estimator is used to simulate the high dimensional set of random variables appearing in the decomposition. The capability of the method to reproduce the non-stationary and non-Gaussian features of random phenomena is illustrated by applications to earthquakes (seismic ground motion) and sea states (wave heights).
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Dates et versions

hal-01314928 , version 1 (24-05-2016)

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I. Zenter, G. Ferré, F. Poirion, Michel Benoit. A biorthogonal decomposition for the identification and simulation of non-stationary and non-Gaussian random fields. Journal of Computational Physics, 2016, 314, pp.1-13. ⟨10.1016/j.jcp.2016.02.067⟩. ⟨hal-01314928⟩
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