Fall 2023 RIT: Machine Learning For Rare Events

The goal of this RIT is to get familiar with and exchange ideas for theoretical and numerical approaches for quantifying stochastic dynamics in complex physical and chemical systems.

Organizers: Dr. Maria Cameron, Margot Yuan, Perrin Ruth, Shashank Sule

Registration

Optional. 1 credit. Course number: AMSC689, Section 0802. See Jessica Sadler for help with registration (jsadler@umd.edu). To earn 1 credit, you need to give a talk.

Schedule

Time/Location: Mondays at 3:00 pm, Kirwan Hall 1310.

Sep 18: Maria Cameron, Complex dynamics of nonlinear oscillators (organizational meeeting).

Sep 25: Aditi Sen, Logistic regression for Massive Data with Rare Events

Suggestions for papers:

You can give a talk on your research or on any paper of your choice if it is related to machine learning or rare events. Below are some suggested papers on model reduction and autoencoders:

Model reduction

  1. L. Maragliano, A. Fischer, E. Vanden-Eijnden, G. Ciccotti, String method in collective variables: minimum energy paths and isocommittor surfaces, 2006

  2. F. Legal and T. Lelievre, Effective dynamics using conditional expectations, Nonlinearity 2010

  3. I. Gyoengy, Mimicking the One-dimensional marginal distributions of processes having an Ito differential, 1986

  4. W. Zhang, C. Hartmann, C. Schuette, Effective dynamics along given reaction coordinate and reaction rate theory, 2016

  5. C. Hartmann, C. Schuette, W. Zhang, Model reduction algorithms for optimal control and importance sampling, 2016

  6. F. Nueske, P. Koltai, L. Boninsegna, C. Clementi, Spectral properties of effective dynamics from conditional expectations, 2021

Autoencoders

  1. W. Chen, A. Ferguson, Molecular enhances sampling with auto encoders: on-the-fly collective variable discovery and accelerated free energy landscape exploration, 2018

  2. W. Chen, A. Tan, A. Ferguson, Collective variable discovery and enhanced sampling using auto encoders: innovations in network architecture and error function design, 2018

  3. C. Wehmeyer and F. Noe, time-lagged auto encoders: deep learning of slow collective variables from molecular kinetics, 2018

  4. Z. Belkacemi, P. Gkeka, T. Lelievre, G. Stoltz, Chasing collective variables using auto encoders and biased trajectories, 2022

  5. E. Crabtree, J. Bello-Rivas, A. Ferguson, I. Kevrekidis, GANs and closures: micro-macro consistency in multiscale modeling, 2022