• 周翔副教授學術報告

    發布時間:2020年11月06日 作者:王洪橋   消息來源:    閱讀次數:[]

    報告題目:Accelerate calculations of transition starts by GPR

    報告人:周翔副教授, 香港城市大學

    報告時間:202011615:00-16:00

    騰訊會議:931 916 378;密碼:1122

    報告摘要:The saddle point (SP) detecting is a grand challenge for computational intensive energy function. The traditional method usually be time-consuming due to the thousands simulation for the derivative information of the energy function. We propose a surrogate based SP detection method which obviously reduce the number of simulations. This method combines a statistical method, Gaussian process regression and a dynamic SP detecting method, Gentle accent dynamic method. We sequentially detecting the SP by GAD and update the surrogate GPR. We use an active learning strategy to detect the points getting data and a quick numerical implementation algorithm is given. Two classical rare event examples are given for comparing the efficiency of the method. A challenge MD example are given.

    報告人簡介: 周翔, 香港城市大學數據科學學院和數學學院副教授、博士生導師。博士畢業于普林斯頓大學,曾在布朗大學做博后研究工作。在J. Comp. Phys. SIAM J. Numer. Anal. SIAM J. Sci. Comput. Nonlinearity J. Chem. Phys.等期刊發表SCI論文10余篇。



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