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- W4296707404 abstract "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." @default.
- W4296707404 created "2022-09-23" @default.
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- W4296707404 date "2022-09-21" @default.
- W4296707404 modified "2023-10-17" @default.
- W4296707404 title "Deep Learning the Functional Renormalization Group" @default.
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- W4296707404 doi "https://doi.org/10.1103/physrevlett.129.136402" @default.
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