2022/02/27 by Domenico Di Sante, Matija Medvidović, Alessandro Toschi +5 · 1 voice
Physics and Astronomy · #Model Reduction and Neural Networks #Quantum many-body systems #Theoretical and Computational Physics #cond-mat.dis-nn #cond-mat.str-el
paper · pdf · doi:10.1103/physrevlett.129.136402
arxiv published 2022/02/27 · openalex publication_date 2022/09/21 · arxiv updated 2023/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
We perform a data-driven dimensionality reduction of the scale-dependent four-point vertex function characterizing the functional renormalization group (FRG) flow for the widely studied two-dimensional t-t' Hubbard model on the square lattice. We demonstrate that a deep learning architecture based on a neural ordinary differential equation solver in a low-dimensional latent space efficiently learns the FRG dynamics that delineates the various magnetic and d-wave superconducting regimes of the Hubbard model. We further present a dynamic mode decomposition analysis that confirms that a small number of modes are indeed sufficient to capture the FRG dynamics. Our Letter demonstrates the possibility of using artificial intelligence to extract compact representations of the four-point vertex functions for correlated electrons, a goal of utmost importance for the success of cutting-edge quantum field theoretical methods for tackling the many-electron problem.