Math Physics Seminar

September 12, 2017 - 2:30pm
309 VAN

“Machine Learning Analysis of Ising Worms” by Professor Yannick Meurice and Mr. Samuel Foreman, Department of Physics & Astronomy, The University of Iowa

Abstract:  We discuss the sampling of high temperature configurations for the two-dimensional Ising model (“worms”).  We show that worm averages  can be used to calculate the thermodynamic  energy and the known logarithmic divergence of the specific heat at the critical temperature. We show numerical evidence supporting the conjecture that the leading eigenvalue of the PCA, a method commonly used to deal with images in machine learning, has a logarithmic divergence at the critical temperature. We discuss the correspondence between the two approaches under coarse graining procedures.