A new low rank approximation and its applications in uncertainty quantification

Speaker:    Prof. Lijian Jiang

Time:        12:00-13:00, May 31

Location:    SIST 1A-402

Host:       Prof. Qifeng Liao

Abstract:

A new low rank approximation is presented for efficient real-time computation of stochastic models. In the approach, a novel variable-separation is used to get a separated representation of the solution for stochastic models in a systematic enrichment manner. A model-driven stochastic basis functions are constructed in the low rank approximation. To significantly decrease the computation complexity for the stochastic basis functions, we construct a hybrid low rank approximation based on multi-fidelity models and multiple models. The proposed approach is explored in uncertainty quantification, e.g., stochastic saddle point problems, Bayesian inversion and data assimilation.

Bio:

姜立建，现为同济大学数学教授。 2012年获得国家“海外高层次青年人才计划”支持。主要研究方向是多尺度方法及其不确定性量化，在SIAM系列杂志， Journal of Computational Physics，Computer Methods in Applied Mechanics and Engineering等科学计算和交叉学科杂志发表论文40多篇。 现担任应用数学综合期刊Journal of Computational and Applied Mathematics 和 《数值计算与计算机应用》的编委。.