no code implementations • 10 Feb 2022 • Denis Boyda, Salvatore Calì, Sam Foreman, Lena Funcke, Daniel C. Hackett, Yin Lin, Gert Aarts, Andrei Alexandru, Xiao-Yong Jin, Biagio Lucini, Phiala E. Shanahan
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies.
no code implementations • 21 Oct 2021 • Dimitrios Bachtis, Gert Aarts, Biagio Lucini
The transition to Euclidean space and the discretization of quantum field theories on spatial or space-time lattices opens up the opportunity to investigate probabilistic machine learning within quantum field theory.
no code implementations • 22 Sep 2021 • Nicholas Sale, Jeffrey Giansiracusa, Biagio Lucini
In particular, we introduce a new way of computing the persistent homology of lattice spin model configurations and, by considering the fluctuations in the output of logistic regression and k-nearest neighbours models trained on persistence images, we develop a methodology to extract estimates of the critical temperature and the critical exponent of the correlation length.
no code implementations • 16 Sep 2021 • Dimitrios Bachtis, Gert Aarts, Biagio Lucini
The precise equivalence between discretized Euclidean field theories and a certain class of probabilistic graphical models, namely the mathematical framework of Markov random fields, opens up the opportunity to investigate machine learning from the perspective of quantum field theory.
no code implementations • 18 Feb 2021 • Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We derive machine learning algorithms from discretized Euclidean field theories, making inference and learning possible within dynamics described by quantum field theory.
no code implementations • 29 Apr 2020 • Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods.