Kernel Principal Component Analysis for Stochastic Input Model Generation (PREPRINT)

Stochastic analysis of random heterogeneous media provides useful information only if realistic input models of the material property variations are used. These input models are often constructed from a set of experimental samples of the underlying random field. To this end, the Karhunen-Loeve K-L expansion, also known as principal component analysis PCA, is the most popular model reduction method due to its uniform mean-square convergence. However, it only projects the samples onto an optimal linear subspace, which results in an unreasonable representation of the original data if they are non-linearly related to each other. In other words, it only preserves the second-order statistics covariance of a random field, which is insufficient for reproducing complex structures. This paper applies kernel principal component analysis KPCA to construct a reduced-order stochastic input model for the material property variation in heterogeneous media. KPCA can be considered as a nonlinear version of PCA. Through use of kernel functions, KPCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the standard Karhunen-Loeve K-L expansion. Thus, this method can model non-Gaussian, non-stationary random fields. In addition, polynomial chaos PC expansion is used to represent the random coefficients in KPCA which provides a parametric stochastic input model. Thus, realizations, which are consistent statistically with the experimental data, can be generated in an efficient way. We showcase the methodology by constructing a low-dimensional stochastic input model to represent channelized permeability in porous media.