In recent years, machine learning techniques penetrated a tremendous variety of scientific fields. In particular, they gave rise to data-driven methods for the study of rare event in complex physical systems such as conformational changes in biomolecules, rearrangements of clusters of interacting particles, etc. These methods truly opened new horizons by enabling us to address problems that used to be intractable due to the curse of dimensionality. They are divided into two families: diffusion map-based and neural network-based. In this RIT we will explore methods for the study of rare events based on machine learning.
Each meeting, one of the participants will give a talk on a paper relevant to the subject of the RIT or on his/her research if it is related to ML and rare events. A list of possible papers to present is given in the overview slides. The participants are welcome to look for other papers on the subject as well.
There is an option for students to register for this RIT for one credit: AMSC689 section 0802. In order to register, students need to contact Jessica Saddler (jsadler at umd dot edu), the AMSC program coordinator, and provide their UID. Students who has registered are expected to give a talk.