Algorithm 945
Algorithm 945
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算法945
DOI：
10.1145/2616912
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. Rowley
中科院分区：
文献类型：
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作者：
Brandt A. Belson;Jonathan H. Tu;C. Rowley
我们描述了一个新的并行Python库，用于模型简化，模态分析和大型系统和数据集的系统识别。我们的库名为modred，可以处理各种各样的问题和任何数据格式。Modred库包含本征正交分解（POD），平衡POD（BPOD）Petrov-Galerkin投影和动态模式分解（DMD）的更有效变体的实现。该库包含这些算法的两个实现，每个都有自己的优点。一种是用于较小和较简单的数据集，需要使用最少的知识，并遵循常见的基于矩阵的公式。第二个，对于更大和更复杂的数据集，保留了向量作为向量空间元素的抽象，因此，允许库处理任意数据格式并简化分布式内存并行化。我们还包括特征系统实现算法（ERA）和观测器/卡尔曼滤波器识别（OKID）的实现。这些方法通常不需要计算并且不并行化。该库设计为易于使用，具有面向对象的设计，并包括全面的自动化测试。在几乎所有情况下，并行化都是在内部完成的，这样使用并行化类的脚本就可以串行或并行运行，而无需任何修改。
We describe a new parallelized Python library for model reduction, modal analysis, and system identification of large systems and datasets. Our library, called modred, handles a wide range of problems and any data format. The modred library contains implementations of the Proper Orthogonal Decomposition (POD), balanced POD (BPOD) Petrov-Galerkin projection, and a more efficient variant of the Dynamic Mode Decomposition (DMD). The library contains two implementations of these algorithms, each with its own advantages. One is for smaller and simpler datasets, requires minimal knowledge to use, and follows a common matrix-based formulation. The second, for larger and more complicated datasets, preserves the abstraction of vectors as elements of a vector space and, as a result, allows the library to work with arbitrary data formats and eases distributed memory parallelization. We also include implementations of the Eigensystem Realization Algorithm (ERA), and Observer/Kalman Filter Identification (OKID). These methods are typically not computationally demanding and are not parallelized. The library is designed to be easy to use, with an object-oriented design, and includes comprehensive automated tests. In almost all cases, parallelization is done internally so that scripts that use the parallelized classes can be run in serial or in parallel without any modifications.