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  • 天元讲堂(11.28)姜立建:A low rank approximation and its applications in uncertainty quantification
  • 浏览量:10 发布人: 发布时间:2019-11-28

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    报告题目A low rank approximation and its applications in uncertainty quantification

    告人:姜立建同济大学

    时间:2019年1128日(星期2:00—3:00

    地点:苏州大学本部精正楼(数学楼)307

      

    摘要A 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.

      

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