Dimension reduction of the Functional Renormalization Group

          @misc{ scivideos_PIRSA:23060031,
            doi = {10.48660/23060031},
            url = {https://pirsa.org/23060031},
            author = {},
            keywords = {Quantum Matter},
            language = {en},
            title = {Dimension reduction of the Functional Renormalization Group},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {jun},
            note = {PIRSA:23060031 see, \url{https://scivideos.org/index.php/pirsa/23060031}}
          }
          

Abstract

ZOOM: https://pitp.zoom.us/j/94595394881?pwd=OUZSSXpzYlhFcGlIRm81Y3VaYVpCQT09 In this work, we use data-driven methods to reduce the dimensionality of the vertex function for the Hubbard model and spin liquid model. By employing a deep learning architecture based on the autoencoder, we show that the functional renormalization group (FRG) dynamics can be efficiently learned. Our approach is compared with other methods, including principal component analysis and dynamic mode decomposition. Our results demonstrate the effectiveness of our proposed approach for understanding the FRG flow in these models.