一种用于大规模宏模型无源性表征的多级自适应采样方法
A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels
摘要：
提出了一种用于大规模线性集总宏模型无源性表征的分层自适应采样方法。这里，大规模是指在动态顺序和特别是输入/输出端口的数量方面。标准的无源性表征方法的基础上的频谱特性的相关的哈密顿矩阵是效率低下或不适用于大规模的模型，由于计算成本过高。本文建立在现有的自适应采样方法，并提出了一种混合的多阶段算法，能够检测到的被动违反有限的计算资源。从广泛的测试结果表明，相对于竞争的方法，在计算要求的重大减少。
英文摘要：
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.
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