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Sparse representation for multiscale models and its application for uncertainty quantification
Date:2016/6/13     Browse:439

Sparse representation for multiscale models and its application for uncertainty quantification

Speaker: Lijian Jiang

Time: Jun 13, 10:30am - 11:30pm.

Location: Room 310, Teaching Center

Abstract:

Stochastic multiscale modeling has become a popular approach to quantify uncertainty in multiscale models.  The combination of multiscale features and complex uncertainty in models leads to great challenge to simulate the models and explore the propagation of uncertainty. To treat the difficulty, it is desirable to construct a sparse representation for the outputs of the multiscale models. In the talk, we present a sparse representation based on reduced multiscale basis methods and investigate its applications for uncertainty quantification of flows in random porous media.

Bio:

Education

Ph.D, Mathematics, Texas A&M University, TX, USA

 

Professional experience

Professor,   Hunan University, China

Research fellow,   Lanl, USA

Postdoctoral fellow,   IMA, University of Minnesota, USA

 

Editorial Service

Associate Editor:  Journal of Computational and Applied Mathematics; Associate Editor:  Journal on Numerical Methods and Computer Applications

 

Research Interests

Numerical analysis, Scientific computing; Advanced numerical methods (e.g., multiscale methods, mimetic finite difference methods, etc.), Porous media applications; Uncertainty quantifications;

 

Research Projects:

1. Multiscale stochastic model reduction methods and some applications (CNSF).

2. Thousand Talents program (class C)

 

SIST-Seminar 16043