Weiqing Ren，National University of Singapore
Room 306, No.5 Science Building
The committor function is a central object in understanding transition events between metastable states in complex systems. It has a very simple mathematical description – it satisfies the backward Kolmogorov equation. However, computing the committor function for realistic systems at low temperatures is a challenging task, due to the curse of dimensionality and the scarcity of transition data. In this talk, I will present a computational approach that overcomes these issues and achieves good performance on complex benchmark problems with rough energy landscapes. The new approach combines deep learning, importance sampling and feature engineering techniques. This establishes an alternative practical method for studying rare transition events among metastable states of complex, high dimensional systems.
Professor Ren Weiqing graduated from Nanjing University with a master’s degree in 1997. He obtained a master’s degree from the Hong Kong University of Science and Technology in 1999. He received his PhD from New York University in 2002. From 2002 to 2005, he worked as a postdoctoral fellow at Princeton Advanced Institute and Princeton University. In 2005, he was an assistant professor at the Courant Institute of New York University, during which he was awarded the Sloan Research Fellowship. In 2011, he joined the National University of Singapore as a faculty member and successively served as an associate professor and a senior researcher at the High Performance Computing Research Institute of the Singapore Research Council. He received the Feng Kang Scientific Computing Award in 2015 and is currently a professor in the Department of Mathematics at NUS.