Applied Mathematics Seminar——Active Learning of Transition State of Free Energy by Gaussian Process Regression
报告人：ZHOU Xiang（City University of Hong Kong）
时间：2023-10-17 10:00-11:00 AM
地点：智华楼四元厅
报告摘要：
In the study of rare event, transition state is one of central concepts, which can regarded as index-1 saddle point (SP) problem on energy landscapes. This talk will review the works the methods of finding these saddle points, including the gentlest ascent dynamics (GAD) and its multiscale method. The main topic is for computationally intensive function such as the free energy surface. The active learning from data science is introduced by combining Gaussian process regression (GPR) and the Gentlest Ascent Dynamics to overcome the prohibitive cost of original problem. We update the samples adaptively in training surrogate Gaussian Process by an active learning strategy of learning while moving. This is joint work with Dr Hongqiao Wang and Dr Shuting Gu.
Bio：
Professor Xiang Zhou received his BSc from Peking University (School of Mathematical Sciences) and PhD from Princeton University (PACM). He holds the associate professor at School of Data Science, City University of Hong Kong now. His major research focus is the study of rare event and computational methods for stochastic models and machine learning algorithms.
