Dynamic Mode Decomposition (DMD) describes complex dynamic processes through a hierarchy of simpler coherent features. DMD is regularly used to understand the fundamental characteristics of turbulence and is closely related to Koopman operators. However, verifying the decomposition, equivalently the computed spectral features of Koopman operators, remains a major challenge due to the infinite-dimensional nature of Koopman operators. Challenges include spurious (unphysical) modes, and dealing with continuous spectra, both of which occur regularly in turbulent flows. Residual Dynamic Mode Decomposition (ResDMD), introduced by (Colbrook & Townsend 2021), overcomes some of these challenges through the data-driven computation of residuals associated with the full infinite-dimensional Koopman operator. ResDMD computes spectra and pseudospectra of general Koopman operators with error control, and computes smoothed approximations of spectral measures (including continuous spectra) with explicit high-order convergence theorems. ResDMD thus provides robust and verified Koopmanism. We implement ResDMD and demonstrate its application in a variety of fluid dynamic situations, at varying Reynolds numbers, arising from both numerical and experimental data. Examples include: vortex shedding behind a cylinder; hot-wire data acquired in a turbulent boundary layer; particle image velocimetry data focusing on a wall-jet flow; and acoustic pressure signals of laser-induced plasma. We present some advantages of ResDMD, namely, the ability to verifiably resolve non-linear, transient modes, and spectral calculation with reduced broadening effects. We also discuss how a new modal ordering based on residuals enables greater accuracy with a smaller dictionary than the traditional modulus ordering. This paves the way for greater dynamic compression of large datasets without sacrificing accuracy.
翻译:动态模式分解( DMD) 描述通过更简单的一致性特性的层次结构的复杂动态进程。 DMD 经常用于理解动荡的基本特征, 并且与Koopman 操作员密切相关。 然而, 校验分解, 相当于 Koopman 操作员的计算光谱特性, 由于Koopman 操作员具有无限的天性, 仍然是一大挑战。 挑战包括假( 无形) 模式, 以及处理连续光谱, 两者都经常在动荡流中发生。 由 ( Colbrook & Townsend 2021) 引入的剩余动态模式分解( ResDMD), 通过数据驱动计算与全无线的Koopman 操作员相关的剩余数据。 ResDMD Computes computra 和伪光谱仍然是由 Koopman 操作员操作员的无限的光谱性特性。 挑战包括虚假的( 包括连续光谱) 光谱度测量测量器, 以及清晰的更小的离子流。 。 。 ResDMDMDMDMDDMD 提供了更强的流, 和核实的流数据, 。 我们的直流 的流的流数据, 和核实如何的流数据。 我们的流 以更精确的直流, 和校的直流, 以更低的直流数据 数据 。