FAST AND ACCURATE MODEL REDUCTION FOR SPECTRAL METHODS IN UNCERTAINTY QUANTIFICATION

Freitas Francisco Damasceno; Pulch Roland; Rommes Joost

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FAST AND ACCURATE MODEL REDUCTION FOR SPECTRAL METHODS IN UNCERTAINTY QUANTIFICATION

机译：不确定度量化中光谱方法的快速精确模型约简

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A fast and accurate model order reduction procedure is presented that can successfully be applied to spectral methods for uncertainty quantification problems. The main novelties include (1) the application of model order reduction to uncertainty quantification problems; (2) the improvement of existing model order reduction methods in order to meet the accuracy and performance requirements; and (3) an efficient approach for systems with many outputs. Numerical experiments for large-scale realistic systems illustrate the suitability and performance (50x speedup while preserving accuracy) for uncertainty quantification problems.

机译：提出了一种快速，准确的模型降阶程序，该程序可成功应用于不确定性量化问题的频谱方法。主要新颖之处包括：（1）模型阶数约简在不确定性量化问题中的应用；（2）改进现有模型降阶方法，以满足精度和性能要求；（3）对于具有许多输出的系统的有效方法。大规模现实系统的数值实验说明了不确定性量化问题的适用性和性能（在保持精度的同时，速度提高了50倍）。

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来源
《International journal for uncertainty quantifications》 |2016年第3期|271-286|共16页
作者
Freitas Francisco Damasceno; Pulch Roland; Rommes Joost;
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作者单位

Univ Brasilia, Dept Elect Engn, BR-70910900 Brasilia, DF, Brazil;

Ernst Moritz Arndt Univ Greifswald, Dept Math & Comp Sci, Walther Rathenau Str 47, D-17487 Greifswald, Germany;

Mentor Graph Corp, 110 Rue Blaise Pascal, Montbonnot St Martin, France;

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正文语种 eng
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关键词
uncertainty quantification; model order reduction; stochastic Galerkin method; spectral method; linear dynamical MIMO system;

机译：不确定度量化模型降阶随机Galerkin方法光谱方法线性动态MIMO系统;

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FAST AND ACCURATE MODEL REDUCTION FOR SPECTRAL METHODS IN UNCERTAINTY QUANTIFICATION

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