題  目：A new reduced order model for parameterized PDEs based on dynamic mode decomposition

内容簡介：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, a new ROM is proposed based on k-nearest neighborhood (KNN) and DMD, namely, KNN-DMD. The KNN can be used to approximate the solution at any given parameter by choosing and averaging the nearest k DMD solutions based on the distance between the given parameter and other parameters. 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.

報告人：高振

報告人簡介：中國海洋大學數學科學學院副院長、博士生導師、山東省“泰山學者”青年專家、山東省高校優秀青年創新團隊帶頭人，一直從事随機計算、計算流體力學、統計學習方法等的研究工作；主持國家重點研發計劃課題、國家某重大科技專項項目、國家自然科學基金等10餘項課題。

時  間：2024年2月18日（周日）下午13：00開始

地  點：騰訊會議：63129598867

熱烈歡迎廣大師生參加!

太阳集团1088vip

2024年2月15日