Yuehaw Khoo
University of Chicago
Thursday, April 28, 2022
3:30 pm - 4:30 pm
154 Hurley Hall
Title: Transition Path Theory with Low Complexity Representations
Abstract: Deep neural-network/tensor method can be used for compressing high-dimensional functions arising from partial differential equations (PDE). In this talk, we focus on using these methods for solving for the committor function. The committor function enables the study the transition processes between metastable states in chemistry applications.
