Faculty of Mechanical Engineering, Technion - Israel Institute of Technology, Haifa 3200003, Israel.
Viterbi Faculty of Electrical and Computer Engineering, Technion - Israel Institute of Technology, Haifa 3200003, Israel.
Chaos. 2022 Dec;32(12):123127. doi: 10.1063/5.0123101.
Dynamic mode decomposition (DMD) is a leading tool for equation-free analysis of high-dimensional dynamical systems from observations. In this work, we focus on a combination of DMD and delay-coordinates embedding, which is termed delay-coordinates DMD and is based on augmenting observations from current and past time steps, accommodating the analysis of a broad family of observations. An important utility of DMD is the compact and reduced-order spectral representation of observations in terms of the DMD eigenvalues and modes, where the temporal information is separated from the spatial information. From a spatiotemporal viewpoint, we show that when DMD is applied to delay-coordinates embedding, temporal information is intertwined with spatial information, inducing a particular spectral structure on the DMD components. We formulate and analyze this structure, which we term the spatiotemporal coupling in delay-coordinates DMD. Based on this spatiotemporal coupling, we propose a new method for DMD components selection. When using delay-coordinates DMD that comprises redundant modes, this selection is an essential step for obtaining a compact and reduced-order representation of the observations. We demonstrate our method on noisy simulated signals and various dynamical systems and show superior component selection compared to a commonly used method that relies on the amplitudes of the modes.
动态模态分解(DMD)是一种从观测中对高维动力系统进行无方程分析的主要工具。在这项工作中,我们专注于 DMD 和延迟坐标嵌入的组合,称为延迟坐标 DMD,它基于对当前和过去时间步的观测进行扩充,以适应广泛的观测分析。DMD 的一个重要用途是通过 DMD 特征值和模态将观测以紧凑的降阶谱表示,其中时间信息与空间信息分离。从时空的角度来看,我们表明,当 DMD 应用于延迟坐标嵌入时,时间信息与空间信息交织在一起,在 DMD 分量上诱导出特定的谱结构。我们对这种结构进行了形式化和分析,称之为延迟坐标 DMD 的时空耦合。基于这种时空耦合,我们提出了一种新的 DMD 分量选择方法。当使用包含冗余模态的延迟坐标 DMD 时,这种选择是获得观测的紧凑和降阶表示的必要步骤。我们在噪声模拟信号和各种动力系统上验证了我们的方法,并展示了与依赖于模态幅度的常用方法相比,优越的分量选择。