In this work, we present a method which determines optimal multi-step dynamic mode decomposition (DMD) models via entropic regression, which is a nonlinear information flow detection algorithm. Motivated by the higher-order DMD (HODMD) method of \cite{clainche}, and the entropic regression (ER) technique for network detection and model construction found in \cite{bollt, bollt2}, we develop a method that we call ERDMD that produces high fidelity time-delay DMD models that allow for nonuniform time space, and the time spacing is discovered by consider most informativity based on ER. These models are shown to be highly efficient and robust. We test our method over several data sets generated by chaotic attractors and show that we are able to build excellent reconstructions using relatively minimal models. We likewise are able to better identify multiscale features via our models which enhances the utility of dynamic mode decomposition.

通过熵回归，我们提出了一种确定最优多步动态模式分解(DMD)模型的方法，该方法是一种非线性信息流检测算法。我们称之为ERDMD的方法产生了高保真度的时间延迟DMD模型，允许非均匀的时间空间，并通过考虑熵回归得出最具信息性的时间间隔，这些模型表现高效且稳健。我们测试了在混沌吸引子生成的几个数据集上的方法，并展示了我们能够使用相对较小的模型构建出优秀的重构模型。此外，我们的模型能够更好地识别多尺度特征，增强了动态模式分解的实用性。

信息熵回归DMD（ERDMD）发现具有信息稀疏性和非均匀时间延迟的模型