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22-28 July 2018
Kellogg Hotel and Conference Center
EST timezone

Machine learning inspired analysis of the Ising model transition

Jul 27, 2018, 3:40 PM
20m
Big Ten A (Kellogg Hotel and Conference Center)

Big Ten A

Kellogg Hotel and Conference Center

219 S Harrison Rd, East Lansing, MI 48824
Theoretical Developments Theoretical Developments

Speaker

Mr Samuel Foreman (University of Iowa)

Description

We illustrate how principal component analysis of simulation data represented as images generated from the worm algorithm, a method to sample the strong coupling contributions, can be used to identify the critical temperature Tc in the Ising model. It is shown that the eigenvalue corresponding to the first principal component of the covariance matrix obtained from pixel ensembles scales logarithmically as one approaches Tc, in a way that is similar to the specific heat. We then illustrate how to block the resulting worm configurations under renormalization group transformations. It is found that curves for the variance of the average number of bonds can be scaled appropriately to illustrate universal behavior under the renormalization group transformation, and that discrepancies can be understood as an effect of approximations.

Primary author

Mr Samuel Foreman (University of Iowa)

Co-authors

Prof. Joel Giedt (Rensselaer Polytechnic Institute) Judah Unmuth-Yockey (Syracuse University) Yannick Meurice (U. of Iowa)

Presentation Materials