论文标题
一种多阶段的自适应抽样方案，用于大规模大型模型的被动表征
A Multi-Stage Adaptive Sampling Scheme for Passivity Characterization of Large-Scale Macromodels
论文作者
论文摘要
本文提出了一种分层自适应采样方案，用于大规模线性总成分大型模块的被动表征。在这里，大规模旨在根据动态顺序，尤其是输入/输出端口的数量。由于过度的计算成本，基于相关哈密顿矩阵的光谱特性的标准被动表征方法对于大规模模型效率低下或不适用。本文以现有的自适应抽样方法为基础，并提出了一种混合多阶段算法，该算法能够通过有限的计算资源来检测违规行为。广泛测试的结果表明，相对于竞争方法，计算要求的大大减少。
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.
