Unsupervised machine learning account of magnetic transitions in the Hubbard model
Unsupervised machine learning account of magnetic transitions in the Hubbard model
We employ several unsupervised machine learning techniques, including autoencoders, random trees embedding, and $t$-distributed stochastic neighboring ensemble ($t$-SNE), to reduce the dimensionality of, and therefore classify, raw (auxiliary) spin configurations generated, through Monte Carlo simulations of small clusters, for the Ising and Fermi-Hubbard models at finite temperatures. Results from a …