[Theory-seminars] Fw: HEP/NP seminar Feb 13 - “Replacing Markov Chain Monte Carlo with Generative Flow Neural Networks” - Kimmy Cushman - Yale University

[Mary Fox]

________________________________
From: Monahan, Christopher <cjmonahan at wm.edu>
Sent: Wednesday, February 5, 2020 1:25 PM
To: Mary Fox <mfox at jlab.org>
Subject: [EXTERNAL] FW: HEP/NP seminar Feb 13 - “Replacing Markov Chain Monte Carlo with Generative Flow Neural Networks” - Kimmy Cushman - Yale University

Hi Mary,

Please could you circulate this to the Theory Group? Thank you!

Regards,
Chris

________________________________
From: Wilkinson, Ellie V <evwilk at wm.edu>
Sent: 05 February 2020 11:48
To: physics2017 at physics.wm.edu <physics2017 at physics.wm.edu>
Subject: HEP/NP seminar Feb 13 - “Replacing Markov Chain Monte Carlo with Generative Flow Neural Networks” - Kimmy Cushman - Yale University

[A person smiling for the camera  Description automatically generated]Speaker: Kimmy Cushman, Yale University
Title:       “Replacing Markov Chain Monte Carlo with Generative Flow Neural Networks”
Host:       C. Monahan
Time:      3:30 pm February 13, 2020
Place:      Physics Department, Small 122





Abstract:
Quantum chromodynamics and other strongly coupled gauge theories are only solvable numerically, and the current state-of-the-art methods are variants of Markov Chain Monte Carlo (MCMC) integration over particle fields defined on a discretized spacetime lattice. Properly sampling from the underlying distribution of lattice configurations is essential to computing correct observables, but traditional MCMC computational limitations place severe constraints on the resolution of simulations which can be performed. In this talk, I will discuss progress made toward replacing Markov chain Monte Carlo approaches with generative flow neural networks for the generation of gauge configurations. I will explain the benefits of using this architecture of neural network, and show our progress in implementing a one-dimensional spin theory. In this toy model, we implement a novel approach to confirming the physical accuracy of the ensembles by using a renormalization group Monte Carlo method to verify that the configurations are in the same universality class as those produced by MCMC.


Cheers,

Ellie Wilkinson
William & Mary Physics
Administrative Coordinator
Small Hall 123
757-221-3503


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