论文标题
使用动态模式分解来预测两次非平衡绿色功能的动力学
Using dynamic mode decomposition to predict the dynamics of a two-time non-equilibrium Green's function
论文作者
论文摘要
计算Kadanoff-baym方程的数值解,这是一组非线性积分微分方程,这是由两次Green的函数所满足的,这些函数来自远离平衡的量子多体系统的多体扰动理论,这是一项具有挑战性的任务。最近,我们成功地应用了动态模式分解(DMD)来构建一个数据驱动的减少订单模型,该模型可用于在小时窗口内从KBE的数字解中推断两次绿色函数的时间对角线。在本文中,我们扩展了先前的工作,并使用DMD来预测两次Green功能的非对角性元素。我们将两次绿色的函数划分为沿两个时间窗口的对角线和分子以及水平和垂直方向的多个一次性函数。我们使用DMD来构建单独的减少订单模型,以在两步过程中预测这些一次性函数的动力学。我们在第一步的两次窗口的一个子频带内沿对角线和几个子谱系推断。在第二步中,我们使用DMD来推断绿色的功能在亚对角线频段之外。我们通过将其应用于两种频段的哈伯德模型问题来证明这种方法的效率和准确性。
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.