报告题目： Reduced order models for parameterized PDEs based on dynamic mode decomposition

报告人：高振 教授 （中国海洋大学）

报告时间：2026年1月17日 周六 15：00-16：00

报告地点：数学院425

摘要： Accurately constructing a reduced order model (ROM) of parameterized partial differential equations (PDEs) has always been the challenging problem in engineering and applied sciences. Dynamic mode decomposition (DMD) is a popular and efficient data-driven method for ROM, however, it is proposed for the model order reduction of time-dependent problems that it is invalid for the parameterized problems. In this talk, ROMs are proposed based on k-nearest neighborhood (KNN) and DMD, namely, KNN-DMD. We apply the proposed method to various parameterized PDEs. The results demonstrate the applicability and efficiency of the proposed KNN-DMD as a real-time ROM for parameterized PDEs. Furthermore, KNN-DMD shows better predictive ability than the POD-based ROMs at the outside of the training time region.

报告人简介：高振教授现任中国海洋大学数学科学学院副院长、博士生导师、山东省“泰山学者”青年专家、山东省高校优秀青年创新团队带头人，主要从事随机计算、计算流体力学等的研究工作；主持国家重点研发计划、国家重大科技专项、国家自然科学基金等20 余项课题。