This paper proposes a hierarchical adaptive sampling scheme for passivity characterization of large-scale linear lumped macromodels. Here, large-scale is intended both in terms of dynamic order and especially number of input/output ports. Standard passivity characterization approaches based on spectral properties of associated Hamiltonian matrices are either inefficient or non-applicable for large-scale models, due to an excessive computational cost. This paper builds on existing adaptive sampling methods and proposes a hybrid multi-stage algorithm that is able to detect the passivity violations with limited computing resources. Results from extensive testing demonstrate a major reduction in computational requirements with respect to competing approaches.

中文翻译：

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用于大规模宏观模型无源表征的多级自适应采样方案

本文提出了一种分层自适应采样方案，用于大规模线性集总宏模型的无源表征。在这里，大规模是指动态顺序，尤其是输入/输出端口的数量。由于计算成本过高，基于相关哈密顿矩阵的光谱特性的标准无源表征方法要么效率低下，要么不适用于大规模模型。本文建立在现有自适应采样方法的基础上，提出了一种混合多级算法，能够以有限的计算资源检测被动违规。广泛测试的结果表明，与竞争方法相比，计算要求大大降低。