Simulation of Highly Skewed Non-Gaussian Stochastic Processes

By George Deodatis, R. Micaletti. 2001

228 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b40cca0b9fa68e360f195646ba4f04f5fed8f1e9

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In Civil and Structural Engineering

In Statistics and Probability

Citations in the corpus

Assessment of spectral representation and Karhunen–Loève expansion methods for the simulation of Gaussian stochastic fields (2007, unknown): “There exist a wide variety of methods related to the translation concept (memory-less non-linear transformation of a Gaussian field to a non-Gaussian one) which are using this technique for the generation of sample functions of the underlying Gaussian field [11,12,21,24,32,46].”
Simulation of non-Gaussian processes using fractile correlation (2004, background): “Deodatis and Micaletti [8] noted that there are some”
Simulation of simply cross correlated random fields by series expansion methods (2008, background): “Their algorithm was shown to have certain difficulties matching the marginal probability distributions in the case when the distributions were highly skewed and Deodatis and Micaletti [11] presented two modifications to it.”
A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic vector process by a translation process with applications in wind velocity simulation (2013, methodology): “Again, some iterative scheme or optimization routine is typically used to determine a ‘‘best-fit’’ underlying Gaussian process [6–15].”
Simulation of strongly non-Gaussian processes using Karhunen–Loeve expansion (2005, background): “The former approach does not work well for strongly non-Gaussian processes, but this limitation has been addressed recently [20].”
Statistical moving load identification including uncertainty (2012, methodology): “Further development has been conducted to extend the application of this approach tomore complicated Gaussian stochastic processes [24–26], non-Gaussian stochastic processes [27,28], etc.”
Statistical reconstruction of two-phase random media (2014, methodology): “To help examine the performance of the proposed method, we have also implemented the simulated annealing method [1] and the iterative nonlinear transformation method [26,28].”
The stochastic finite element method: Past, present and future (2009, methodology): “Methods [21,34,100,147] are iterative because their objective is to match the prescribed probabilistic characteristics at the individual sample level through spatial averaging, whereas the technique described in [80] requires only one step because its objective is to meet the same goal through ensem”