Synopsis
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.
Program
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 or rare events.
Registration
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 Sadler (jsadler at umd dot edu), the AMSC program coordinator, and provide their UID. Students who has registered are expected to give a talk.
Some Suggestions For Papers to Present
Schedule for Spring 2022
Organizational Meeting Slides
Maria Cameron
Friday February 4, 12PM
Modeling the fracture of hydrogels
Manyuan Tao
Friday February 11th, 12PM
Solving the committor equation using tensor trains
Margot Yuan
Friday February 25th, 12PM
Computing committors using Mahalanobis diffusion maps
Luke Evans
Friday March 4th, 12PM
Deep learning for solving backward stochastic differential equations
Zhirui Li
Friday April 1st, 12PM
Simulation for rare events in quantum error correction
Raley Roberts
Friday April 8th, 12PM
A new constructive heuristic driven by machine learning for the traveling salesman problem/Error analysis for target-measure diffusion maps
Shuhan Kou/Shashank Sule
Friday April 22nd, 12PM
Physics-Informed Neural Networks/The Deep Ritz Method
Lang Song/Jiaxing Liang
Friday April 29th, 12PM
Wasserstein gradient flows
Jiaqi Wang
Friday May 6th, 12PM
Schedule for Fall 2021
An overview. Slides
Maria Cameron
Friday September 10, 2PM
Diffusion maps applied to molecular dynamics
Luke Evans
Friday September 24, 2PM
Quantifying rare events with the aid of neural networks
Margot Yuan
Friday October 8, 2pm
Analysis of activation functions for neural networks (based on A. Townsend's papers)
Manyuan Tao
Friday October 15, 2pm
Self-assembly of hydrocarbons
Christopher Moakler
Friday October 22, 2pm
Mean-field analysis and scaling limits for neural networks (based on works by J. Sirignano and K. Spiliopoulos) (cancelled)
Maria Cameron
Friday November 5, 2pm
An Introduction to Density Functional Theory. Slides
Ryan Synk
Friday November 19, 2pm
Spectralnet
Shashank Sule
Friday December 3, 2pm