Passivity preserving parametric model-order reduction for non-affine parameters
Passivity preserving parametric model-order reduction for non-affine parameters
复制标题
非仿射参数的被动性保留参数模型降阶
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
R. Dyczij
中科院分区：
文献类型：
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作者：
O. Farle;S. Burgard;R. Dyczij
参数化模型降阶（parametermodel-order reduction，pMOR）已成为分析多参数大系统的成熟技术。然而，非仿射参数的处理仍然构成重大挑战，因为基于投影的降阶方法不能直接应用。一个常见的补救措施是建立仿射参数依赖关系近似，但目前的提取方法不考虑重要的系统属性，如被动性，考虑。本文提出了一种新的降阶方法，该方法保留了无源性、互惠性和因果性，并适用于广泛的线性时不变（LTI）系统。我们提出的理论所建议的方法，并证明其实用性的数值例子从计算电磁学。
Parametric model-order reduction (pMOR) has become a well-established technology for analysing large-scale systems with multiple parameters. However, the treatment of non-affine parameters is still posing significant challenges, because projection-based order-reduction methods cannot be applied directly. A common remedy is to establish affine parameter-dependencies approximately, but present extraction methods do not take important system properties, such as passivity, into account. This article proposes a new order-reduction approach that preserves passivity, reciprocity and causality and applies to a wide class of linear time-invariant (LTI) systems. We present the theory of the suggested method and demonstrate its practical usefulness by numerical examples taken from computational electromagnetics.