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Xiang Ma
Researcher at ExxonMobil
Publications - 12
Citations - 1163
Xiang Ma is an academic researcher from ExxonMobil. The author has contributed to research in topics: Stochastic process & Random field. The author has an hindex of 10, co-authored 12 publications receiving 1110 citations. Previous affiliations of Xiang Ma include Cornell University.
Papers
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Journal ArticleDOI
An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations
Xiang Ma,Nicholas Zabaras +1 more
TL;DR: An adaptive sparse grid collocation strategy using piecewise multi-linear hierarchical basis functions and Hierarchical surplus is used as an error indicator to automatically detect the discontinuity region in the stochastic space and adaptively refine the collocation points in this region.
Journal ArticleDOI
An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations
Xiang Ma,Nicholas Zabaras +1 more
TL;DR: The proposed method provides accurate results for stochastic dimensionality as high as 500 even with large-input variability and the efficiency of the proposed method is examined by comparing with Monte Carlo (MC) simulation.
Journal ArticleDOI
An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method
Xiang Ma,Nicholas Zabaras +1 more
TL;DR: In this paper, an adaptive hierarchical sparse grid collocation (ASGC) method is used for constructing an interpolant to the solution of the forward model in this prior space which is large enough to capture all the variability/uncertainty in the posterior distribution of the unknown parameters.
Journal ArticleDOI
Kernel principal component analysis for stochastic input model generation
Xiang Ma,Nicholas Zabaras +1 more
TL;DR: In this paper, the authors apply kernel principal component analysis (KPCA) to construct a reduced-order stochastic input model for the material property variation in heterogeneous media.
Kernel Principal Component Analysis for Stochastic Input Model Generation (PREPRINT)
Xiang Ma,Nicholas Zabaras +1 more
TL;DR: KPCA is applied to construct a low-dimensional stochastic input model to represent channelized permeability in porous media and enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the standard Karhunen-Lo`eve (K-L) expansion.