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A Shared Memory Parallel Implementation of the IRKA Algorithm for H_2 Model Order Reduction (Extended Abstract)

机译:用于降低H_2模型阶数的IRKA算法的共享内存并行实现(扩展摘要)

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摘要

Dealing with large scale dynamical systems is important in many industrial applications. In design and optimization, it is often impossible to work with the original large scale system due to the necessary time for simulation. In order to make this process economically acceptable one has to replace these large scale models by smaller ones which preserve the essential properties and dynamics of the original one. After the computation of a reduced order model a fast simulation is possible. The reduced order model can be obtained by different techniques minimizing the reduction error with respect to different system norms. One of the most common techniques in this application area is balanced truncation which approximates with respect to the H_∞-norm. A parallel implementation of this is available in PLiCMR. In this contribution we focus on the parallel implementation of the IRKA algorithm employing the H_2-norm for measuring the error.
机译:在许多工业应用中,应对大型动力系统很重要。在设计和优化中,由于需要进行仿真,因此通常无法使用原始的大型系统。为了使该过程在经济上可以接受,必须用较小的模型代替这些大型模型,这些模型可以保留原始模型的基本特性和动力学。在计算降阶模型之后,可以进行快速仿真。可以通过不同的技术来获得降阶模型,该技术可以使相对于不同系统规范的降阶误差最小。该应用领域中最常见的技术之一是相对于H_∞范数近似的平衡截断。 PLiCMR中提供了对此的并行实现。在这一贡献中,我们集中于采用H_2范数来测量误差的IRKA算法的并行实现。

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  • 会议地点 Helsinki(FI)
  • 作者

    Martin Koehler; Jens Saak;

  • 作者单位

    Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems, Sandtor-Str. 1, 39106 Magdeburg, Germany;

    Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems, Sandtor-Str. 1, 39106 Magdeburg, Germany;

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