Analysis of variance-based mixed multiscale finite element method and applications in stochastic two-phase flows

Jia Wei, Guang Lin*, Lijian Jiang, Yalchin Efendiev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The stochastic partial differential systems have been widely used to model physical processes, where the inputs involve large uncertainties. Flows in random and heterogeneous porous media is one of the cases where the random inputs (e.g., permeability) are often modeled as a stochastic field with high-dimensional random parameters. To treat the high dimensionality and heterogeneity efficiently, model reduction is employed in both stochastic space and physical space. An analysis of variance (ANOVA)-based mixed multiscale finite element method (MsFEM) is developed to decompose the high-dimensional stochastic problem into a set of lower-dimensional stochastic subproblems, which require much less computational complexity and significantly reduce the computational cost in stochastic space, and the mixed MsFEM can capture the heterogeneities on a coarse grid to greatly reduce the computational cost in the spatial domain. In addition, to enhance the efficiency of the traditional ANOVA method, an adaptive ANOVA method based on a new adaptive criterion is developed, where the most active dimensions can be selected to greatly reduce the computational cost before conducting ANOVA decomposition. This novel adaptive criterion is based on variance-decomposition method coupled with sparse-grid probabilistic collocation method or multilevel Monte Carlo method. The advantage of this adaptive criterion lies in its much lower computational overhead for identifying the active dimensions and interactions. A number of numerical examples in two-phase stochastic flows are presented and demonstrate the accuracy and performance of the adaptive ANOVA-based mixed MsFEM.

Original languageEnglish (US)
Pages (from-to)455-477
Number of pages23
JournalInternational Journal for Uncertainty Quantification
Issue number6
StatePublished - 2014

Bibliographical note

Publisher Copyright:
© 2014 by Begell House, Inc.


  • Adaptivity
  • Analysis of variance
  • Mixed multiscale finite element method
  • Polynomial chaos
  • Stochastic partial differential equation
  • Two-phase flow
  • Uncertainty quantification

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Discrete Mathematics and Combinatorics
  • Control and Optimization


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