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arXiv 1707.02839math.NAcs.NA
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在有限时间区间内对大系统进行平衡截断模型降阶

Balanced truncation model order reduction in limited time intervals for large systems

Max Planck Institute for Dynamics of Complex Technical Systems(马普复杂技术系统动力学研究所)

Patrick Kürschner

AI总结:

本文研究了在有限时间区间内对大系统进行平衡截断降阶的方法,重点讨论了如何高效实现该方法,利用有理Krylov子空间方法解决矩阵指数函数和Lyapunov方程,并展示有限时间截断降阶的数值秩可能更小,从而提高时间区间内的降阶模型精度。

摘要:

本文研究了利用时间受限平衡截断方法对大规模系统进行模型降阶。该方法将经典的平衡截断框架限制在给定的有限时间区间内。本文重点讨论了在大系统维度情况下,该降阶方法的高效数值实现。我们讨论了解决由此产生的矩阵指数函数和Lyapunov方程的数值方法,这些方程需要求解低秩近似。我们的主要工具是有理Krylov子空间方法。我们还讨论了Lyapunov方程解的特征值衰减和数值秩。这些结果以及数值实验表明,根据最终时间范围,时间受限平衡截断的Lyapunov解的数值秩可能比标准平衡截断更小。在数值实验中,我们测试了计算涉及Lyapunov解低秩因子的方法,并展示了时间受限平衡截断可以生成在考虑的时间区域内具有更高精度的降阶模型。

英文摘要:

In this article we investigate model order reduction of large-scale systems using time-limited balanced truncation, which restricts the well known balanced truncation framework to prescribed finite time intervals. The main emphasis is on the efficient numerical realization of this model reduction approach in case of large system dimensions. We discuss numerical methods to deal with the resulting matrix exponential functions and Lyapunov equations which are solved for low-rank approximations. Our main tool for this purpose are rational Krylov subspace methods. We also discuss the eigenvalue decay and numerical rank of the solutions of the Lyapunov equations. These results, and also numerical experiments, will show that depending on the final time horizon, the numerical rank of the Lyapunov solutions in time-limited balanced truncation can be smaller compared to standard balanced truncation. In numerical experiments we test the approaches for computing low-rank factors of the involved Lyapunov solutions and illustrate that time-limited balanced truncation can generate reduced order models having a higher accuracy in the considered time region.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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