Using dynamic mode decomposition to predict the dynamics of a two-time non-equilibrium Green's function

TL;DR

This paper extends the use of dynamic mode decomposition (DMD) to predict off-diagonal elements of two-time Green's functions in quantum many-body systems, enabling efficient extrapolation of complex dynamics beyond initial data.

Contribution

The authors develop a two-step DMD-based method to accurately predict off-diagonal elements of two-time Green's functions, improving modeling of non-equilibrium quantum dynamics.

Findings

DMD accurately predicts off-diagonal Green's function elements.

The method efficiently extrapolates complex quantum dynamics.

Application to a two-band Hubbard model demonstrates effectiveness.

Abstract

Computing the numerical solution of the Kadanoff-Baym equations, a set of nonlinear integral differential equations satisfied by two-time Green's functions derived from many-body perturbation theory for a quantum many-body system away from equilibrium, is a challenging task. Recently, we have successfully applied dynamic mode decomposition (DMD) to construct a data driven reduced order model that can be used to extrapolate the time-diagonal of a two-time Green's function from numerical solution of the KBE within a small time window. In this paper, we extend the previous work and use DMD to predict off-diagonal elements of the two-time Green's function. We partition the two-time Green's function into a number of one-time functions along the diagonal and subdiagonls of the two-time window as well as in horizontal and vertical directions. We use DMD to construct separate reduced order models to predict the dynamics of these one-time functions in a two-step procedure. We extrapolate along diagonal and several subdiagonals within a subdiagonal band of a two-time window in the first step. In the second step, we use DMD to extrapolate the Green's function outside of the sub-diagonal band. We demonstrate the efficiency and accuracy of this approach by applying it to a two-band Hubbard model problem.