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报告题目：  An adaptive model order reduction method for parametrized evolution equations

报告人：  张永金  博士

摘要：Model order reduction (MOR) has emerged as an important tool in reducing the computational burden of large-scale systems, particularly in real-time or many-query contexts, e.g., optimization, control, and uncertainty quantification. In this talk, we present a brief introduction of projection-based MOR methods and show some applications of MOR in chemical engineering. In particular, efficient output error estimates are derived to adaptively construct reduced-order models with desired accuracy. Applications to Burgers Equations and chromatographic models demonstrate that the reduced-order models are very efficient in reducing the computational cost when applied to accelerate PDE constrained optimization and uncertainty quantification.

时间：2019年9月27日（周五）下午4:30-5:30

地点：逸夫楼1417

欢迎大家参加！