PIRSA:23060031

Video URL

https://pirsa.org/23060031

Dimension reduction of the Functional Renormalization Group

(2023). Dimension reduction of the Functional Renormalization Group. Perimeter Institute for Theoretical Physics. https://pirsa.org/23060031

Dimension reduction of the Functional Renormalization Group. Perimeter Institute for Theoretical Physics, Jun. 12, 2023, https://pirsa.org/23060031 

          @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}}
          }
Jiawei Zang
Talk numberPIRSA:23060031
DOI10.48660/23060031
Source RepositoryPIRSA
Collection
Talk Type Conference
Subject

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.