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Exploiting Extended Krylov Subspace for the Reduction of Regular and Singular Circuit Models
arXiv - CS - Numerical Analysis Pub Date : 2020-07-03 , DOI: arxiv-2007.01948 Chrysostomos Chatzigeorgiou, Dimitrios Garyfallou, George Floros, Nestor Evmorfopoulos, and George Stamoulis
arXiv - CS - Numerical Analysis Pub Date : 2020-07-03 , DOI: arxiv-2007.01948 Chrysostomos Chatzigeorgiou, Dimitrios Garyfallou, George Floros, Nestor Evmorfopoulos, and George Stamoulis
During the past decade, Model Order Reduction (MOR) has become key enabler
for the efficient simulation of large circuit models. MOR techniques based on
moment-matching are well established due to their simplicity and computational
performance in the reduction process. However, moment-matching methods based on
the ordinary Krylov subspace are usually inadequate to accurately approximate
the original circuit behavior. In this paper, we present a moment-matching
method which is based on the extended Krylov subspace and exploits the
superposition property in order to deal with many terminals. The proposed
method can handle large-scale regular and singular circuits and generate
accurate and efficient reduced-order models for circuit simulation.
Experimental results on industrial IBM power grids demonstrate that our method
achieves an error reduction up to 83.69% over a standard Krylov subspace
method.
中文翻译:
利用扩展的 Krylov 子空间来减少规则和奇异电路模型
在过去十年中,模型降阶 (MOR) 已成为高效仿真大型电路模型的关键推动因素。基于力矩匹配的 MOR 技术由于其在减少过程中的简单性和计算性能而被很好地建立。然而,基于普通 Krylov 子空间的矩匹配方法通常不足以准确地近似原始电路行为。在本文中,我们提出了一种基于扩展 Krylov 子空间并利用叠加特性来处理许多终端的矩匹配方法。所提出的方法可以处理大规模的规则和奇异电路,并为电路仿真生成准确有效的降阶模型。
更新日期:2020-09-09
中文翻译:
利用扩展的 Krylov 子空间来减少规则和奇异电路模型
在过去十年中,模型降阶 (MOR) 已成为高效仿真大型电路模型的关键推动因素。基于力矩匹配的 MOR 技术由于其在减少过程中的简单性和计算性能而被很好地建立。然而,基于普通 Krylov 子空间的矩匹配方法通常不足以准确地近似原始电路行为。在本文中,我们提出了一种基于扩展 Krylov 子空间并利用叠加特性来处理许多终端的矩匹配方法。所提出的方法可以处理大规模的规则和奇异电路,并为电路仿真生成准确有效的降阶模型。